BUSCADOR RECOLECTA

The data generated or analysed during this study are obtained included in the clinical trial registry name, URL and registration number: TREATMENT-HV; EUDRACT 2018-000744-26; https://eudract.ema. europa.eu/. File1 Adverse events after the treatment with antipsychotics, Short-term treatment with aripiprazole and olanzapine had a significant influence on the metabolic parameters. However, it seems that aripiprazole provokes less severe metabolic changes., European Comission ITN Treatment H2020-MSCA-ITN-721236, Peer reviewed

File 1. This xls files shows the data related to the main charateristics used for the development and validation of the new method as long as the measures for letting assure the repeatability and intermediate precision and accuracy values. The results obtained for the pharmacokinetic parameters are also included in the file File 2 This jpg file shows the product ion spectra and chemical structures of the antipsychotics and their metabolites obtained by collision-induced dissociation (CID) of the indicated precursor ions [M+H]+. The fragmentation patterns of all analytes are indicated by an arrow on their chemical structure of each analyte. The results are presented as the percentage of counts versus Mass-to-Charge (m/z). All mass peaks have been normalized to the most abundant, A simple and sensitive liquid chromatography-tandem mass spectrometry method was developed and validated in human plasma for the simultaneous determination of aripiprazole (ARI) and its metabolite dehydro-aripiprazole (DARI); olanzapine (OLA), risperidone (RIS), paliperidone (PAL), quetiapine (QUE), clozapine (CLO) and caffeine (CAF). CAF is included to the method because it can have an influence on drug metabolism due to competitive inhibition. The above mentioned compounds and their isotope-labeled internal standards were extracted from 200 µL human plasma samples by both, effective phospholipids-eliminating three-step microelution-solid-phase extraction (µ-SPE) and protein precipitation (PPT) for comparison. A combination of formic acid (0.2%)-acetonitrile (pH 3.0; 65:35, v/v) was used as mobile phase and the chromatogram was run under gradient conditions at a flow rate of 0.6 mL/min. Run time lasted 6 min, followed by a re-equilibration time of 3 min. All analytes were monitored by mass spectrometric detection operating in multiple reaction monitoring mode and the method was validated covering the corresponding therapeutic ranges: 0.18-120 ng/mL for ARI, 0.25-80 ng/mL for DARI, 1.00-100 ng/mL for OLA, 0.70-60 ng/mL for RIS, 0.20-30 ng/mL for PAL, 0.50-160 ng/mL for QUE, 0.50-1000 ng/mL for CLO, and finally 1200-3700 ng/mL for CAF. The method was validated based on the recommendations of regulatory agencies through tests of precision, accuracy, extraction recovery, identity confirmation, trueness, matrix effect, process efficiency, stability, selectivity, linearity and carry-over effect fulfilling the guideline requirements. Our µ-SPE method results in the elimination of more than 99% of early eluting and more than 92% of late-eluting phospholipids compared to PPT. Additionally, the method was successfully applied for quantifying ARI and OLA plasma concentrations from healthy volunteers., European Comission ITN Treatment H2020-MSCA-ITN-721236, Peer reviewed

File 1. Demographic characteristics of the volunteers from the aripiprazole study. File 2. Prolactin pharmacokinetic parameters after aripiprazole and ibuprofen administration. This xls file shows the modifications in the prolactin concentrations after the administration of aripiprazole or ibuprofen A It is been shown the prolactin concentrations versus time in women and men treated with aripiprazole compared to ibuprofen File 3 Genotype Matrix. The xls file show the detected genetic polymorphism associated with the effects of aripiprazole File 4 Aripiprazole and dehydro-aripiprazole pharmacokinetic parameters. File 5 The influence of polymorphisms on prolactin concentrations., Aripiprazole treatment in schizophrenic patients was previously associated with lower or normalized prolactin levels. Genetic variants in cytochrome P450 (CYP) (CYP2D6), dopamine receptor (DRD2, DRD3) and serotonin receptor (HTR2A, HTR2C) genes were previously associated with antipsychotic-induced hyperprolactinaemia. Our aim was to evaluate whether aripiprazole affects prolactin secretion and its relationship with pharmacogenetics. Thirty-one healthy volunteers receiving a 10-mg single oral dose of aripiprazole were genotyped for 12 polymorphisms in CYP2D6, DRD2, DRD3, HTR2A and HTR2C genes by qPCR. Aripiprazole and dehydro-aripiprazole plasma concentrations were measured by HPLC-MS/MS. Prolactin concentrations of the 31 volunteers taking aripiprazole and 12 volunteers receiving ibuprofen were determined by ELISA. Prolactin concentrations after ibuprofen intake were considered as control, since it is known to cause no effect. Prolactin concentrations were slightly higher in the aripiprazole group compared to the ibuprofen group. All prolactin pharmacokinetic parameters were higher in females than in males. CYP2D6 poor and intermediate metabolizers had notably higher prolactin Cmax and AUC0-12 than normal and ultrarapid metabolizers. The DRD3 rs6280 polymorphism affected prolactin levels: volunteers carrying Ser/Ser genotype had significantly lower prolactin levels than volunteers carrying the Gly allele. Furthermore, HTR2C rs3813929 C/C homozygotes had significantly lower prolactin levels than T allele carriers. Nevertheless, aripiprazole did increase prolactin levels compared to ibuprofen., European Comission ITN Treatment H2020-MSCA-ITN-721236, Peer reviewed

File 1. Adverse events_TREATMENT The xls file shows relevant information about the treatment with aripiprazole and olanzapine, as well as about their possible adverse effects and the frequency with which they occur. File 2. Genotype Matrix. The xls file show the detected genetic polymorphism associated with the effects of aripiprazole and olanzapine, Aripiprazole altered pupil contraction. Olanzapine caused significant prolactin and weight elevation. Glucose levels in glucose tolerance test were higher after olanzapine treatment. Moreover, olanzapine had more cardiovascular effects than aripiprazole. However, aripiprazole was associated to more psychiatric and nervous system adverse drug reactions. Many polymorphisms may influence pupillometric and metabolic parameters along with cardiovascular changes and adverse events. Moreover, several polymorphisms had an effect on aripiprazole, dehydro-aripiprazole and olanzapine pharmacokinetics. It seems that aripiprazole provokes less severe metabolic and cardiovascular changes, however, more adverse drug reactions were registered to it compared to olanzapine., European Comission ITN Treatment H2020-MSCA-ITN-721236, Peer reviewed

Folder 1. Analysis of in vivo chronic dietary treatment with both olanzapine and aripiprazole (5.5-6 mg/kg/d) for 6 months in WT females to assess disturbances in glucose metabolism as well as beta-cell dysfunction. Metabolic tests have been done before sacrifice: glucose tolerance test (GTT), insulin tolerance test (ITT), and glucose-stimulated insulin secretion (GSIS). The xls file shows the statistics analysis and raw data analysis of the parameters measured. The immunofluorescence and immunohistochemistry analysis are also included in xls file. The pzfx files can be opened with GraphPad Prism 8 XML Project show the relationships between some of the parameters showed in xls files. Folder 2. In vitro data in INS-1 cells. The xls files showed the quantification of the short-term impact of olanzapine (1-6 µM) and the long-term impact of both olanzapine and aripiprazole (1-6 µM) in glucose-stimulated insulin secretion (GSIS) and the western blot quantification of the molecular machinery associated with ER stress activation (mainly PERK/eIF2a and IRE1-alpha) in INS-1 cells after olanzapine/aripiprazole treatment with different concentrations (1-6 µM) for 4h. The pzfx files can be opened with GraphPad Prism 8 XML Project and show the relationships between some of the parameters showed in xls files., The incidence of Type 2 Diabetes Mellitus (T2DM) is reaching epidemic proportions. Recent investigations have demonstrated that long-term treatment with Second Generation Antipsychotics (SGAs), the main-line treatment for schizophrenia, can induce T2DM. Beta cell dysfunction is proposed as a plausible mechanism by which SGAs cause T2DM, but the process remains largely unknown. In this Thesis, we have investigated whether two unrelated SGAs, olanzapine, a common prescribed SGA with diabetogenic properties, and aripiprazole, a more recently developed SGA with less explored metabolic-side effects, can impact on beta cells. We analyzed beta cell functionality and pancreatic islet plasticity in two in vivo studies: female mice treated with olanzapine for 6 weeks via intraperitoneal and female mice fed an olanzapine- or aripiprazole-supplemented diet for 6 months. Additionally, we conducted gene expression analysis in islets of mice receiving the medicated diet and in vitro studies to evaluate beta cell functionality and the molecular mechanisms associated to the treatments. Our results evidenced that long-term treatment with olanzapine or aripiprazole induced weight gain, glucose intolerance and beta cell dysfunction, but the mechanisms behind these alterations are specific for each SGA. Whereas olanzapine effects in the pancreas in female mice seem to be dependent on an obesogenic-like phenotype, it activated endoplasmic reticulum (ER) stress in both INS-1 cells and pancreatic islets. Alleviation of olanzapine-induced ER stress with Tauroursodeoxycholic acid (TUDCA) recovered insulin secretion, suggesting that inhibition of insulin secretion by olanzapine is dependent on ER stress activation. On the other hand, aripiprazole treatment during 6 months induced serotonin production through tryptophan hydroxylase 1 (TPH1) activation in pancreatic islets. Moreover, beta cell hypertrophy and higher beta cell mass were found in aripiprazole-treated mice concomitantly to the activation of mTORC1/S6. Additionally, ex vivo experiments using pancreatic islets revealed that aripiprazole inhibition of insulin secretion was due to a reduction in calcium entry into the beta cell. Until now, regulation of the serotonergic system in islets has been associated to beta cell compensation in pregnancy and postnatal growth. Thus, in this Thesis, we have described for the first time the modulation of the serotonergic system in pancreatic islets by pharmacological treatment with aripiprazole, showing that serotonin production induced by this SGA plays a critical role in intra-islet functionality and beta cell mass and it might also explain other metabolic disturbances associated with aripiprazole treatment, European Comission ITN Treatment H2020-MSCA-ITN-721236, Peer reviewed

The dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Please, read the full ODbL 1.0 license text for the exact terms that apply. Users of the dataset are free to: Share: copy, distribute and use the database, either commercially or non-commercially. Create: produce derivative works from the database. Adapt: modify, transform and build upon the database. Under the following conditions: Attribution: You must attribute any public use of the database, or works produced from the database. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the original database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL, This research was funded by a predoctoral contract ACIF/2018/077 (to R.S.-D.) and grant PROMETEO/2020/014 from the Regional Government of Valencia (Generalitat Valencia-na), grant BIO2017-87828-C2-1-P and PID2020-116940RB-I00 from the Spanish Ministry of Science, Innovation and Universities, and FEDER funds (ESF, European Social Fund)., List of experimental data: Figure 2: Figure 2.xlsx Figure 2A.SC-Ura 20mM FAS.jpg Figure 2A.SC-Ura 2mM FAS.jpg Figure 2A.SC-Ura 40mM FAS.jpg Figure 2A.SC-Ura 500Fz.jpg Figure 2A.SC.jpg Figure 2C.jpg Figure 2D.jpg Figure 3: Figure 3A.SC-Ura Galactose.jpg Figure 3A.SC-Ura Galactose_1.jpg Figure 3A.SC-Ura Galactose_2.jpg Figure 3A.SC-Ura Glucose_1.jpg Figure 3A.SC-Ura Glucose_2.jpg Figure 3B.SC-Ura-Met 20mM FAS.jpg Figure 3B.SC-Ura-Met 3mM FAS.jpg Figure 3B.SC-Ura-Met 40mM FAS.jpg Figure 3B.SC-Ura-Met 5mM FAS.jpg Figure 3B.SC-Ura-Met.jpg SC-Ura glucosa.jpg Figure 4 & S1 Figure 4 & S1.xlsx Figure 5 & S2 Figure 5 & S2. Xlsx Figure 5.SC-Ura-Met 3mM FAS.Replicate 1. aGFP 10sec.jpg Figure 5.SC-Ura-Met 3mM FAS.Replicate 1. Ponceau.jpg Figure 5.SC-Ura-Met 3mM FAS.Replicate 2. aGFP 10sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 2. aGFP 20sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 2. aGFP 30sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 2. aPgk1.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 2. Ponceau.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 3. aGFP 10sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 3. aGFP 20sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 3. aGFP 30sec.tif Figure 5.SC-Ura-Met 3mM FAS.Replicate 3. Ponceau.jpg Figure 5.SC-Ura-Met.Replicate 1. aGFP 10sec.tif Figure 5.SC-Ura-Met.Replicate 1. aGFP 20sec.tif Figure 5.SC-Ura-Met.Replicate 1. aGFP 30sec.tif Figure 5.SC-Ura-Met.Replicate 1. Ponceau.jpg Figure 5.SC-Ura-Met.Replicate 2. aGFP 10sec.tif Figure 5.SC-Ura-Met.Replicate 2. aGFP 20sec.tif Figure 5.SC-Ura-Met.Replicate 2. aGFP 30sec.tif Figure 5.SC-Ura-Met.Replicate 2. aPgk1.tif Figure 5.SC-Ura-Met.Replicate 2. Ponceau.jpg Figure 5.SC-Ura-Met.Replicate 3. aGFP 10sec.tif Figure 5.SC-Ura-Met.Replicate 3. aGFP 20sec.tif Figure 5.SC-Ura-Met.Replicate 3. aGFP 30sec.tif Figure 5.SC-Ura-Met.Replicate 3. Ponceau.jpg Figure 6: Figure 6.PTEF2CCC1 SC-Ura-Met-3mMFAS-DIC.tif Figure 6.PTEF2CCC1 SC-Ura-Met-3mMFAS-FM4-64.tif Figure 6.PTEF2CCC1 SC-Ura-Met-3mMFAS-GFP.tif Figure 6.PTEF2CCC1 SC-Ura-Met-3mMFAS-Merge.tif Figure 6.PTEF2CCC1 SC-Ura-Met-DIC.tif Figure 6.PTEF2CCC1 SC-Ura-Met-FM4-64.tif Figure 6.PTEF2CCC1 SC-Ura-Met-GFP.tif Figure 6.PTEF2CCC1 SC-Ura-Met-Merge.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-3mMFAS-DIC.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-3mMFAS-FM4-64.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-3mMFAS-GFP.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-3mMFAS-Merge.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-DIC.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-FM4-64.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-GFP.tif Figure 6.PTEF2NtCCC1 SC-Ura-Met-Merge.tif, Peer reviewed

Nombre de la variable y descripción: Year (Año de toma de datos / Year), LayingDate (Fecha de puesta / laying date), Habitat (Habitat (Robledal:1 / Pinar: 2) / Habitat (Oak:1 / Pine: 2)), Mate (Pareja única, con hembra primaria (Mon) o hembra secundaria con ayuda (SecA) o secundaria sin ayuda (SecNoA)/ female mating as primary (Mon), secondary with male help (SecA) or secondary without help (SecNopA)), Age (Edad / Age), MaxPApril (Precipitación máxima de abril / Maximum precipitation in April), MaxPMay (Precipitación máxima de May / Maximum precipitation in May), NAOw (Índice de NAO en invierno / NAO index in winter), PrecipMinLD (Precipitación mínima durante la fecha de puesta / Minimum precipitation during laying), MaxTApril (Temperatura máxima de abril / Maximum temperature in April), TempMaxPreLD (Temperatura máxima durante el período prepuesta / Maximum temperature during prelaying period), PrecipMinPreLD (Precipitación mínima durante el período prepuesta / Minimum precipitation during prelaying period), TempMinLD (Temperatura mínima durante la fecha de puesta / Minimum temperature during laying), MinTMay (Temperatura mínima de Mayo / Maximum temperature in May), MinTApril (Temperatura mínima de Abril / Minimum temperature in April), Female (Identidad de la hembra / Female identity)., The origin of natural selection is found in the environmental heterogeneity that determines a variation in relative fitness among phenotypes. However, individuals in wild populations are exposed not a single but to a plethora of biotic and abiotic environmental factors. Surprisingly, the relative influence of multiple environmental conditions on relative fitness of phenotypes has rarely been tested in wild populations. Identifying the main selection agent(s) is crucial when the target phenotype is tightly linked to reproduction and when temporal variation in selection is expected to affect evolutionary response. By using individual-based information from a short-lived migratory passerine, the pied flycatcher (Ficedula hypoleuca), we studied the relative influence of 28 temperature- and precipitation-based factors at local and global scales on selection on breeding time (egg laying) at the phenotypic level over 29 breeding seasons. Selection penalised late breeders using number of recruits as proxy of fitness. Minimum temperatures in April and May were the environmental drivers that best explained selection on laying date. In particular, there was negative directional selection on laying date mediated by minimum temperature in April being strongest in colder years. In addition, non-linear selection on laying date was shaped by minimum temperatures in May, with selection on laying date changing from null to negative as the breeding season advances. The intensity of selection on late breeders increased when minimum temperatures in May were highest. Our results illustrate the complex influence of environmental factors on selection on laying date in wild bird populations. Despite minimum temperature in April being the only variable that changed overtime, its increase did not induce a shift in laying date in the population. In this songbird population stabilizing selection has led to a three-decade stasis in breeding time. We suggest that the influence of local climatic variables on selection in addition to global climatic trends, may constrain phenotypic change., Financiación: PID2019-104835GB-I00; CGL2015-70639-P/BOS; CGL2014-55969-P; CGL2015-70639-P; ARAID., Peer reviewed

The data set consists of: - a numerical quantitative file saved both in .xlsx and .ods formats: “OLEUM_Dataset_SPME-GC-MS_xlsx.xlsx”, “OLEUM_Dataset_SPME-GC-MS_ods.ods”. - a README file: “OLEUM_Dataset SPME-GC-MS_README_rtf.rtf”.-- Content of the file OLEUM_Dataset_SPME-GC-MS_xlsx/ods: SPME-GC-MS: This sheet presents the repeatability and reproducibility values, expressed as relative standard deviation (RSD%), for three quantification methods (QM1, QM2 and QM3). The three quantification methods are described in Casadei et al., 2021 (Food Control, Vol. 123, 2021, 107823, p. 107823. doi: 10.1016/j.foodcont.2020.107823). Additionally, the concentrations (min and max) determined by the 5 labs for each of 15 samples are shown., This data corresponds to an inter-laboratory study (5 labs) applying a harmonized SPME-GC-MS method to analyze volatile compounds on 15 samples. The data shows the min-max concentration and the repeatability and reproducibility results. This work is relevant to know the performance of this method to be applied as a supporting tool for sensory assessment of virgin olive oil. The data are presented for 18 volatile compound that were selected for their contribution to positive attributes (fruitiness) and sensory defect. This work was developed in the context of the project OLEUM “Advanced solutions for assuring authenticity and quality of olive oil at global scale”, funded by the European Commission within the Horizon 2020 Programme (2014–2020, GA no. 635690)., OLEUM (Advanced solutions for assuring the overall authenticity and quality of olive oil), funded by European Union, Horizon 2020 Programme. Grant Agreement num. 635690; http://www.oleumproject.eu/., Peer reviewed

Supplementary material.-- 1 file, This Excel file is the Supplementary Table 1 of the book chapter entitled "Nitrogen And Phosphorus Cycling Through Marine Sponges: Physiology, cytology, genomics, and ecological implications". It includes the 33 studies published before early 2021 in which ammonium, nitrite, nitrate, and/or phosphate net fluxes have been measured in marine sponges, which have been used in the overall calculations made in the chapter, This study has been partially supported by grant MICIU-PID2019-108627RB-I00 to Manuel Maldonado, No

The Euro-Mediterranean Submarine landSlide database (EMSS21) displays information collected from both the literature and geophysical records. Data from the literature is collected from georeferenced map figures, from which the outline of the landslide deposit and the landslide scar have been digitized. Most information such as age, volume, typology, inferred trigger mechanism and relationship to fluid flow features and metadata is extracted from the relevant article. Area of the deposit and landslide runout is determined from the digitized polygons and polylines, volume (if not available) is determined from area and mean thickness. If the latter is not available, the volume is determined from the Area-Volume relationship for landslides where volume has already been determined. When the data originates directly from geophysical records the outline and scars have been determined from geomorphological interpretation of swath bathymetric maps and interpretation of submarine landslides on a variety of seismic records. In such instances, age information originates from a variety of methods presenting a wide range of accuracies, from radiometric dating to well-seismic correlation as well as ages inferred from mean sedimentation rates. In many instances, landslides identified in the literature are dated with an age epoch only., Disclaimer: The EMSS21 is open to later additions and improvements. For these reasons and due to its intrinsic nature, the EMSS21 cannot be guaranteed to be complete, accurate and updated in any part, and will be subjected to successive revisions. Although the Institut de Ciències del Mar of CSIC makes every effort to supply the best available information, no warranty, expressed or implied, is provided as to the accuracy and reliability of all the data supplied in the EMSS21. Users are cautioned to consider carefully the nature of the data and information before using it for decisions that concern personal or public safety. Conclusions drawn from the EMSS21, or actions undertaken on the basis of its contents, are the sole responsibility of the user., The Euro-Mediterranean Submarine landSlide database (EMSS21) is a catalogue of submarine landslides of the Mediterranean Sea and the European continental margins of the Atlantic and Arctic Oceans. The catalogue is compiled from data available in the literature as well as information collected from geophysical data and not published in the scientific literature. The data set includes polygons and polylines for the landslide deposits and landslide scars as well as information relative to age, volume, area, runout, thickness, typology, scar elevation, geological setting, depth, slope, inferred trigger mechanism, relationship to fluid flow features as well as the relevant metadata. The catalogue includes submarine landslides that span the Miocene to Present day. Nevertheless a clear bias exist towards submarine landslides of younger age, particularly for the smaller events, as these events are difficult to map/identify on lower resolution geophysical data sets typically collected in deep-water and large sub-surface depths. The catalogue aims to offer improved understanding of mass-wasting processes, the potentially resulting tsunamis and derived geohazard. The EMSS21 displays information collected from both the literature and geophysical records. Data from the literature is collected from georeferenced map figures, from which the outline of the landslide deposit and the landslide scar have been digitized. Most information such as age, volume, typology, inferred trigger mechanism and relationship to fluid flow features and metadata is extracted from the relevant article. Area of the deposit and landslide runout is determined from the digitized polygons and polylines, volume (if not available) is determined from area and mean thickness. If the latter is not available, the volume is determined from the Area-Volume relationship for landslides where volume has already been determined. When the data originates directly from geophysical records the outline and scars have been determined from geomorphological interpretation of swath bathymetric maps and interpretation of submarine landslides on a variety of seismic records. In such instances, age information originates from a variety of methods presenting a wide range of accuracies, from radiometric dating to well-seismic correlation as well as ages inferred from mean sedimentation rates. In many instances, landslides identified in the literature are dated with an age epoch only., Data compilation has been possible thanks to projects: Submarine landslides and Their impact on European continental margins (SLATE) - H2020-MSCA-ITN-2016-721403. ImagiNg large SeismogenIc and tsunamiGenic structures of the Gulf of Cadiz with ultra-High resolution Technologies (INSIGHT). - CTM2015-70155-R., The Euro-Mediterranean Submarine landSlide database (EMSS21) is a catalogue of submarine landslides of the Mediterranean Sea and the European continental margins of the Atlantic and Arctic Oceans. The catalogue is compiled from data available in the literature as well as information collected from geophysical data and not published in the scientific literature. The data set includes polygons and polylines for the landslide deposits and landslide scars as well as information relative to age, volume, area, runout, thickness, typology, scar elevation, geological setting, depth, slope, inferred trigger mechanism, relationship to fluid flow features as well as the relevant metadata. The catalogue includes submarine landslides that span the Miocene to Present day. Nevertheless a clear bias exist towards submarine landslides of younger age, particularly for the smaller events, as theseevents are difficult to map/identify on lower resolution geophysical data sets typically collected in deep-water and large sub-surface depths. The catalogue aims to offer improved understanding of mass-wasting processes, the potentially resulting tsunamis and derived geohazard., Peer reviewed