BUSCADOR RECOLECTA

Retrospective data from 147 selected patients that underwent strabismus surgery between 2013-2016. Includes information concerning the type and onset of the strabismus, relevant medical records, awake state eye deviation and the intraoperative strabismus inspection.

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STM/STS and XPS data acquried at IMDEA Nanociencia laboratories, The software to open the STP files is WSxM.

This is the underlying data of the publication: "Osmium(II) Tethered Half-Sandwich Complexes: pH-dependent Aqueous Speciation and Transfer Hydrogenation in Cells" (DOI: 10.1039/D1SC01939B). Data of Fig. 2, 3, 4, S25, S26, S27, S28, S29, S30, S31, S32, S33, S34, S35, S36, S37, S38, S39, and Table 1, 2, 3, S5 and S6 is included., A description of the convention used for compound names is attached in the .zip file.

STM images, STS data, XAS/XMCD/XMD data, The software to open the STP files is WSxM.

R scripts showing how to use climwin package with tree-ring width and anatomy chronologies. The databases needed to use the scripts are included., [FILES] 1. climwin with dendro and anatomy.R R script in which climwin is used to study the growth/anatomy-climate relationships of 5 species with weekly time resolution. 2. climwin with the river flow.R R script in which climwin is used to study the growth-river flows relationships of 2 sites with monthly time resolution. 3. Pinus sylvestris model.R R script in which climwin is used to fit a multiple linear regression. 4. RingWidths.csv Database of detrended growths and anatomical variables needed to run the R scripts. Abbreviations: LA - lumen area CWT - cell wall thickness ew - earlywood lw - latewood Ps - Pinus sylvestris (Corbalán site) Aa - Abies alba (Paco Ezpela site) VA1 - Valdelinares (Pinus uncinata) AL - Alcalá de la Selva (Pinus sylvestris) CO - Olmedilla (Pinus nigra) AC - Alto de Cabra (Pinus pinaster) VH - Valbona (Pinus halepensis) 5. climate.rds Database of climate needed to run the R scripts. Abbreviations: T - Temperature Tmax - Maximum temperature Tmin - Minimum temperature P - Precipitation spei - Standardized Evapotranspiration Precipitation Index using a range of time scales (1, 3, 6, 9, 12, 24, 36 and 48 months) over which water deficits and surplus accumulate are considered. 6. Fraxinus.csv Database of detrended growths of Fraxinus needed to run the R scripts. 7. River flow.csv Database of river flow needed to run the R scripts. 8. readme.txt txt file explaining the details of the data. (2021-07-01), [METHODOLOGY] We aim to identify the most likely climate variables driving the growth and wood anatomy of the species using climwin package. We used the weekly resolved climate data and a randomization technique to find, for each climate variable, the most relevant period of the year in which climate was most related to growth according to climwin. To identify the most likely climate predictors of the growth and wood anatomy features and the most relevant time window (the most influential period of the year for individual climate variables), we fitted simple linear regressions with the growth/anatomy variables as the response variables and the climate variables as predictors. The mean of each factor in each time window considered was used as the aggregate statistics. For each factor all possible window lengths (periods of year) at weekly resolution (but monthly resolution for the flow river) was calculated and the one with the lowest ΔAICc compared to the null model (i.e., including the intercept only) was selected. Finally, randomization tests were calculated using 1000 repetitions to calculate pΔAICc (the likelihood that a climatic signal is real). October 1 of the previous year was established as the threshold for the beginning of the windows and November 31 of the year of growth as the limit for the end of the windows. A minimum length of two weeks was pre-defined. A multiple linear regression were fitted using P. sylvestris pine lumen area chronology, without distinguishing between earlywood and latewood, as the response variable and including the climate variables found to be statistically significant. For building the model with climwin we followed this procedure: (i) among the simple linear models calculated with climwin for the response variable, the model with the lowest ∆AICc was selected; (ii) using this model as baseline model, we introduced the rest of climatic variables one by one in order to fit all possible two-factor models, obtaining for each model ∆AICc, climate windows and p∆AICc; and (iii) the models with p∆AICc < 0.05 were selected. Finally, only a model with two climate variables met this condition. If more significant models with different climatic variables had been found, the whole process would have to be repeated including the model with two climatic factors with lower ∆AICc in the baseline model. Multicollinearity was avoided by controlling the variance inflation factor (VIF)., Ministerio de Economía y Competitividad: CGL2015-69186-C2-1-R Agencia Estatal de Investigación: RTI2018-096884-B-C31, Peer reviewed

3 tables, 30 figures, Supplementary Material for the article https://doi.org/10.1016/j.ecolind.2020.107141, Table S1. Summary of the model structure fitted to each species’ body size data.-- Table S2. Summary table indicating the body size reference points and source of information for each species’ length at maturity.-- Figure S1. Percentage of annual change for the body size of each species at catch for the period 2000-2018 in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S2. Time series of the estimated indices of abundance for the 20 species analysed from 2000 to 2018 in the Galician coast (NE Atlantic) taken from Alonso-Fernández et al. (2019) and updated up to year 2018.-- Figure S3. Percentage of change by year for each species index of abundance for the period 2000-2018 in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S4. Time series of the skewness of the body size frequency distribution for the 20 species analysed over the period 2000 to 2018.-- Figure S5. Slope of the linear trend of body size skewness for each species in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S6. Residual check for the model fitted to Trisopterus luscus body size data.-- Figure S7. Residual check for the model fitted to Pollachius pollachius body size data.-- Figure S8. Residual check for the model fitted to Mullus surmuletus body size data.-- Figure S9. Residual check for the model fitted to Dicentrarchus labrax body size data.-- Figure S10. Residual check for the model fitted to Conger conger body size data.-- Figure S11. Residual check for the model fitted to Labrus bergylta body size data.-- Figure S12. Residual check for the model fitted to Diplodus sargus body size data.-- Figure S13. Residual check for the model fitted to Scophthalmus maximus body size data.-- Figure S14. Residual check for the model fitted to Scophthalmus rhombus body size data.-- Figure S15. Residual check for the model fitted to Solea solea body size data.-- Figure S16. Residual check for the model fitted to Solea senegalensis body size data.-- Figure S17. Residual check for the model fitted to Pegusa lascaris body size data.-- Figure S18. Residual check for the model fitted to Platichthys flesus body size data.-- Figure S19. Residual check for the model fitted to Scyliorhinus canicula body size data.-- Figure S20. Residual check for the model fitted to Raja undulata body size data.-- Figure S21. Residual check for the model fitted to Sepia officinalis body size data.-- Figure S22. Residual check for the model fitted to Octopus vulgaris body size data.-- Figure S23. Residual check for the model fitted to Loligo vulgaris body size data.-- Figure S24. Residual check for the model fitted to Maja brachydactyla body size data.-- Figure S25. Residual check for the model fitted to Necora puber body size data.-- Table S3. Values for all explanatory variables used for predictions for each species' model (Fig. 4 and Fig. 5 in the main text and Fig. S26).-- Figure S26. Estimated (±95 C.I.) variation in body size at catch with depth for the 20 species.-- Figure S27. Plots of the DFA model fitted to the predicted body size at catch for each species in ICES division 8.c in the Galician coast (NE Atlantic).-- Figure S28. Plots of the DFA model fitted to the predicted body size at catch for each species in ICES division 9.a in the Galician coast (NE Atlantic).-- Figure S29. Relationship between (a) the rate of change in body size (% · year-1) and (b) the rate of change in relative abundance (% · year-1) with the average proportion of immature individuals caught (in number, ImC).-- Figure S30. Relationship between the rate of change in body size (% · year-1) with the time trend of body size skewness, Peer reviewed

XRF, biogeochemical and pollen results of B4 depression sediments, Sanetti Plateau (Bale Mountains, Ethiopia), Peer reviewed

All_region_-_community_-_familes_by_samples.xlsx = The sample-by-taxa spreadsheet used in the all-region whole community analysis, i.e. "taxaxsamples" in the Dryad file "Example script to calculate biological metrics in biomonitoR.R", All_region_-_community_-_env._variables_and_bio._metrics.xlsx = A spreadsheet listing - for all samples used in the all-region whole community analysis - methods details, environmental variables and biological response variables, the latter calculated in biomonitoR, All_region_-_highRR_-_familes_by_samples.xlsx = The sample-by-taxa spreadsheet used in the all-region 'high RR' analysis, All_region_-_highRR_-_env._variables_and_bio._metrics.xlsx = A spreadsheet listing - for all samples used in the all-region 'high RR' analysis - methods details, environmental variables and biological response variables, All_region_-_community_-_STAR_ICMi_only.xlsx = A spreadsheet listing - for East Mediterranean samples used in the whole community analyses - the region-specific biomonitoring indices STAR_ICMi and its ASPT (average score per taxon) as calculated following Buffagni et al. (2006), All_region_-_highRR_-_STAR_ICMi_only.xlsx = A spreadsheet listing - for East Mediterranean samples used in the 'high RR' analyses - the region-specific biomonitoring indices STAR_ICMi and its ASPT (average score per taxon) as calculated following Buffagni et al. (2006), Fuzzy_coding_of_traits.csv = A spreadsheet showing the calculation of fuzzy-coded scores for each trait. The final column for each trait (e.g. G for "Maximum potential size") is based on the preceding columns for that trait (e.g. E and F for "Maximum potential size"). For example, in row 10, no individuals have a maximum potential size ≤ .25 cm (0*4, where 4 is the trait weight shown in row B) and 75% of individuals have a maximum potential size > 0.25-0.5 cm (0.75*4); therefore (0*4)+(0.75*4)=3., Genus-level_analyses.xlsx = A multi-tab spreadsheet showing the sample-by-taxa matrix and community metrics for each region/dataset used in the genus-level analyses described in Appendix S1.4, 1. Rivers are dynamic ecosystems in which both human impacts and climate-driven drying events are increasingly common. These anthropogenic and natural stressors interact to influence the biodiversity and functioning of river ecosystems. Disentangling ecological responses to these interacting stressors is necessary to guide management actions that support ecosystems adapting to global change., 2. We analysed the independent and interactive effects of human impacts and natural drying on aquatic invertebrate communities—a key biotic group used to assess the health of European freshwaters. We calculated biological response metrics representing communities from 406 rivers in eight European countries: taxonomic richness, functional richness and redundancy, and two biomonitoring indices that indicate ecological status. We analysed metrics based on the whole community and a group of taxa with traits promoting resistance and/or resilience (‘high RR’) to drying. We also examined how responses vary across Europe in relation to climatic aridity., 3. Most community metrics decreased independently in response to impacts and drying. A richness-independent biomonitoring index (the average score per taxon; ASPT) showed particular potential for use in biomonitoring, and should be considered alongside new metrics representing high RR diversity, to promote accurate assessment of ecological status., 4. High RR taxonomic richness responded only to impacts, not drying. However, these predictors explained little variance in richness and other high RR metrics, potentially due to low taxonomic richness. Metric responsiveness could thus be enhanced by developing region-specific high RR groups comprising sufficient taxa with sufficiently variable impact sensitivities to indicate ecological status., 5. Synthesis and applications. Our results inform recommendations guiding the development of metrics to assess the ecological status of dynamic river ecosystems—including those that sometimes dry—thus identifying priority sites requiring further investigation to identify the stressors responsible for environmental degradation. We recommend concurrent consideration of richness-independent biomonitoring indices (such as an ASPT) and new high RR richness metrics that characterize groups of resistant and resilient taxa for region-specific river types. Interactions observed between aridity, impacts and drying evidence that these new metrics should be adaptable, promoting their ability to inform management actions that protect river ecosystems responding to climate change., European Cooperation in Science and Technology, Award: CA15113, Peer reviewed

20 files, Mussels (Mytilus galloprovincialis) were exposed during 24 hours to a waterborne infection with 10E8 CFU/ml Vibrio splendidus (reference strain LGP32) in the tank water. Five biological replicates were used for each infected and control conditions, APDAPD3401230109.mgf.-- APDAPD3401230109.raw.-- APDAPD3401230110.mgf.-- APDAPD3401230110.raw.-- APDAPD3401230111.mgf.-- APDAPD3401230111.raw.-- APDAPD3401230112.mgf.-- APDAPD3401230112.raw.-- APDAPD3401230113.mgf.-- APDAPD3401230113.raw.-- APDAPD3401230114.mgf.-- APDAPD3401230114.raw.-- APDAPD3401230115.mgf.-- APDAPD3401230115.raw.-- APDAPD3401230116.mgf.-- APDAPD3401230116.raw.-- README.txt.-- checksum.txt.-- peptides.pep.xml, Peer reviewed