[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Growing Degree Day (GDD) is a day in which the average temperature is above 5.5°C. It is the number of degrees above this threshold that counts as a Growing Degree Day. For example if the average temperature for a specific day is 6°C, this would contribute 0.5 Growing Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Growing Degree Days. Given the data shows the annual sum of Growing Degree Days, this value can be above 365 in some parts of the UK.Annual Growing Degree Days are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of GDD to previous values. What are the possible societal impacts?Annual Growing Degree Days indicate if conditions are suitable for plant growth. An increase in GDD can indicate larger crop yields due to increased crop growth from warm temperatures, but crop growth also depends on other factors. For example, GDD do not include any measure of rainfall/drought, sunlight, day length or wind, species vulnerability, or plant dieback in extremely high temperatures. GDD can indicate increased crop growth until temperatures reach a critical level above which there are detrimental impacts on plant physiology.GDD does not estimate the growth of specific species and is not a measure of season length.What is a global warming level?Annual Growing Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Growing Degree Days, an average is taken across the 21 year period. Therefore, the Annual Growing Degree Days show the number of growing degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named 'GDD' (Growing Degree Days), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘GDD 2.5 median’ is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. ‘GDD 2.5 median’ is ‘GDD_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘GDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Growing Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

A study involving several patients followed over three years is conducted and the time until the event occurs for each of them is recorded.

Person A, for example, is followed from the start of the study until getting the event at week 5; his survival time is 5 weeks and is not censored. Person B also is observed from the start of the study but is followed to the end of the 12-week study period without getting the event; the survival time here is censored because we can say only that it is at least 12 weeks. Person C enters the study between the second and 3rd week and is followed until he withdraws from the study at 6 weeks; this person’s survival time is censored after 3.5 weeks. And so forth...

A table of the survival time data for the cohort patients is presented. For each person, we have given the corresponding survival time up to the event’s occurrence or up to censorship. We have indicated in the last column whether this time was censored or not (with 1 denoting failed and 0 denoting cen- sored). For example, the data for person C is a survival time of 3.5 and a censorship indicator of 0, whereas for person F the survival time is 3.5 and the censorship indicator is 1.

This dataset was established during a one year project under the IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer) for the harmonisation of maturity data acquisition methods for bony fish of commercial interest, with the help of scientific campaign CGFS, EVHOE, IBTS and ACCOBIOM (Auber et al., 2021, Laffargue et al.,1987, Le Roy et al., 1988).

This dataset contains 4133 standardised gonad’s macroscopic photos of 61 species of fish of commercial interests collected along the European coastal water and the Caribbean Sea. The scale used throughout this project is the ICES maturity scale “WKASMSF” (ICES, 2018). To have more details about the photography process used for photos in this database, check the “Fish gonads’ photography protocol” from Le Meleder et al. (2022).

This dataset is associated with a GitHub page hosting tools to generate maturity identification forms for fish of commercial interest. To have more detail about identification forms files and have the latest update, check the GitHub page “MaturityScaleTools” (LM-Anna/MaturityScaleTools: Maturity scale tools to identify visual maturity phases (github.com)).

This dataset is meant to be enriched with time. Photos may be added to complete the missing maturity phases for every species of the world. To have more details about the dataset or to add new photos, please contact annalemeleder@orange.fr or laurent.dubroca@ifremer.fr.

Images:

• Photo_MATURITY.zip : archive in zip format of 4133 macroscopic photographs of gonads (.JPG; 2Mo-6Mo; sRGB; 1080p). Each photo was taken with the same camera (OLYMPUS / Tough F2.0), on the same white background, with homogeneous lighting to avoid glints from overexposure. Since there are no duplicated photos’ names because all photos were taken with the same camera, names correspond to the one generated by the camera. Photos are sorted under three levels of directories :

  • First level : species’ scientific name (Example : Dicentrarchus labrax) : there are currently 61 different species listed

  • Second level : F or M : the sex, with F from females and M for male

  • Third level : A, B, C, D, E or F : the maturity phases of the ICES 2018 scale. In each folder are assigned the corresponding gonadic photos.

Data frames:

• photo_mat.xlsx (13 columns / 4133 rows): data table (Excel format) listing all photos in the Photo_MATURITY database, as well as the data associated with the photos. The data table is presented as followed, for each photo :

  • Name : Name of the photo

  • Type : Type of gonad photo (INT = inside without organs, INT ORG = inside with organs, EXT = outside, EXT OUV = outside and open, FLUANT = fluent)

  • sppeng : English vernacular name of the species or species group established for identification forms

  • Species : Scientific name of the species or species group established for identification guides

  • Sex : Sex of the fish (M = male, F = female)

  • phase ID : visually estimated maturity phase (ICES WKASMSF scale : A, B, C, D, E or F)

  • Link : Link to the photo, to change depending on your path to the downloaded dataset =LIEN_HYPERTEXTE(« (Your path to the dataset)\Photo_MATURITE\« &H_n& »\« &E_n& »\« &F_n& »\« &A_n& ».JPG »)*

  • spplatTRUE : Scientific name of the species without taking species groups into account

  • sppengTRUE : English vernacular name of the species without taking species groups into account

  • Date : Date the photo was added to the dataset (the year correspond to the year the photo was took)

  • Campaign : Survey during which the photo was taken

  • Area : Geographical area (ICES or not) where the scientific survey occurred (Caribbean sea = Caribbean waters area, IVb-c = ICES area for the IBTS campaign, NA = unknown area, VIId = ICES area for NourManche campaign, VIId/VIIe = ICES area for CGFS campaign, VIIg/VIIj/VIIh/VIIIa-b = ICES area for EVHOE campaign)

  • Commentary : Comments about the photo.

CAUTION : When using this database, please make sure to modify the link to the photos in the “Link” column with the link where you downloaded the Photo_MATURITY.zip file, and to check if it works by clicking it.

*_n = row number

1. MISSION ATLANTIC The MISSION ATLANTIC project is an EU-funded initiative that focuses on understanding the impacts of climate change and human activities on these ecosystems. The project aims to map and assess the current and future status of Atlantic marine ecosystems, develop tools for sustainable management, and support ecosystem-based governance to ensure the resilience and sustainable use of ocean resources. The project brings together experts from 33 partner organizations across 14 countries, including Europe, Africa, North, and South America. MISSION ATLANTIC includes ten work packages. The present published dataset is included in WP3, which focuses on mapping the pelagic ecosystems, resources, and pressures in the Atlantic Ocean. This WP aims to collect extensive spatial and temporal data to create 3D maps of the water column, identify key vertical ecosystem domains, and assess the pressures from climate change and human activities. More specifically, the dataset corresponds to the fishing intensity presented in the Deliverable 3.2, which integrates data from various sources to map the distribution and dynamics of present ecosystem pressures over time, providing crucial insights for sustainable management strategies. 2. Data description 2.1. Data Source Fishing intensity estimates from the Global Fishing Watch initiative (GFW) (Kroodsma et al. 2018), who applies machine learning algorithms to data from Automatic Identification Systems (AIS), Vessel Monitoring Systems (VMS), and vessel registries, has been used for the year 2020. This machine learning approach has been able to distinguish between fishing and routing activity of individual vessels, while using pattern recognition to differentiate seven main fishing gear types at the Atlantic Ocean scale (Taconet et al., 2019). The seven main fishing vessel types considered are: trawlers, purse seiners, drifting longliners, set gillnets, squid jiggers, pots and traps, and other. In this work we have aggregated these into pelagic, seabed and passive fishing activities to align with our grouping of ecosystem components. The GFW data has some limitations: AIS is only required for large vessels. The International Maritime Organization requires AIS use for all vessels of 300 gross tonnage and upward, although some jurisdictions mandate its use in smaller vessels. For example, within the European Union it is required for fishing vessels at least 15m in length. This means that in some areas the fishing intensity estimates will not include the activity of small vessels operating near shore. AIS can be intentionally turned off, for example, when vessels carry out illegal fishing activities (Kurekin et al. 2019). In the GFW dataset, vessels classified as trawlers include both pelagic and bottom trawlers. As trawlers are included in the bottom fishing category, it is highly likely that the data overestimates the effort on the seafloor and underestimates it on the water column. 2.2. Data Processing 1. Data download from the GFW portal. 2. Using R: Add daily files and aggregate fishing hours by fishing gear and coordinates: library(data.table)## Load data fileIdx = list.files(".../fleet-daily-csvs-100-v2-2020/", full.names = T) ## Loop colsIdx = c("geartype", "hours", "fishing_hours", "x", "y") lapply(fileIdx, function(xx) { out = data.table (x = NA_real_, y = NA_real_, geartype = NA_character_) tmp = fread(xx) tmp[, ":=" (y = floor(cell_ll_lat * 10L) / 10L, x = floor(cell_ll_lon * 10L) / 10L)] tmp = tmp[, ..colsIdx] h = tmp[, c(.N, lapply(.SD, sum, na.rm = T)), by = .(x, y, geartype)] outh = data.table::merge.data.table(out, h, by = c("x", "y", "geartype"), all=TRUE) fwrite(outh, ".../GFW_2020_0.1_degrees_and_gear_all.csv", nThread = 14, append = T) }) Group fishing gears into main fishing groups: library(dplyr)library(tidyr)## Load data fishing <- read.csv(".../GFW_2020_0.1_degrees_and_gear_all.csv", sep=",", dec=".", header=T, stringsAsFactors = FALSE) ## Grouping fishing gears (fishing, pelagic, bottom, passive) # unique(fishing$geartype) fishing$group <- NA fishing$group[which(fishing$geartype == "fishing")] = "fishing" # Unknown fishing$group[fishing$geartype %in% c("trollers", "squid_jigger", "pole_and_line", "purse_seines", "tuna_purse_seines", "seiners", "other_purse_seines", "other_seines", "set_longlines", "drifting_longlines")] <- "pelagic" fishing$group[fishing$geartype %in% c("trawlers", "dredge_fishing")] <- "bottom" fishing$group[fishing$geartype %in% c("set_gillnets", "fixed_gear", "pots_and_traps")] <- "passive" ## Total fishing hours (by fishing and position) fish_gr <- fishing %>% group_by(x, y, group) %>% summarise(gfishing_hours = sum(fishing_hours)) Pivot table in order to have fishing groups in columns. Each row corresponds to the coordinates of the left corner of the grid cell (0.1 decimal degrees): ## Pivoting table (fishing groups in columns) fish_gr3 <- fish_gr %>% pivot_wider(names_from = "group", values_from = "gfishing_hours", va