Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Model output from Wilson et al paper

Model output used in the publication by N. J. Wilson, C. A. Vreugdenhil, B. Gayen, E W. Hester (submitted for review).

opencc-by-4.0Jul 2023View details →

Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage - MODEL OUTPUT

This is the repository for the model output and controls pertaining to the simulation experiments performed in "Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage" by Nathaniel Fillman, Andreas Schmittner, and Karin Kvale. Citation and DOI for parent publication will be updated here when available. See https://github.com/fillmann/variable-stoichiometry for model code.

Optimization output files

Output file containing input data and the results of optimization of LAB-TEA at the B3LYP/6-31+G(d) level of theory using Gaussian 09 program package. The resulting optimized structure is then used as the starting geometry for generating parameters for the LAB-TEA molecule.

opencc-by-4.0Dec 2022View details →

World Offshore Macro Algae Production Potential (WOMAPP) netcdf output data

This set of two zip files contains netcdf output files simulated with the WOMAPP model published as: <ul> <li>van Oort, P.A.J., Verhagen, A., van der Werf, A.K., 2023. Can seaweeds feed the world? Modelling world offshore seaweed production potential. Ecological modelling</li> </ul> The two zip files contain model output for optimistic and pessimistic scenarios, referring to literature based data on seaweed nutrient requirements: <ul> <li>in the optimistic scenario: <ul> <li>cold temperature seaweed species group is NOT sensitive to high waves</li> <li>intermediate temperatures and warm temperature seaweed species groups: lower nutrient requirements</li> </ul> </li> <li>in the pessimistic scenario: <ul> <li>cold temperature seaweed species group is sensitive to high waves</li> <li>intermediate temperatures and warm temperature seaweed species groups: higher nutrient requirements</li> </ul> </li> </ul> Within each zip file, the following netcdf files can be found: <table> <thead> <tr> <th scope="row"> Filename </th> <th scope="col">Contents</th> </tr> </thead> <tbody> <tr> <th scope="row"> WOMAPP_legendmonths.txt </th> <td>QGIS legend file with colourscheme for number of suitable months (range 0-12)</td> </tr> <tr> <th scope="row"> WOMAPP_legendsuitability.txt </th> <td>QGIS legend file with colourscheme for site suitability (range 0.0-1.0)</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityAll_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months. Suitabilities outside the EEZ are all shown as zero (0.0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityAll_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months. Suitabilities outside the EEZ are all shown as zero (0.0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityAll_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months. Suitabilities outside the EEZ are all shown as zero (0.0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityGt_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8). Outside the EEZ number of suitable months is set to zero (0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityGt_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8). Outside the EEZ number of suitable months is set to zero (0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityGt_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8). Outside the EEZ number of suitable months is set to zero (0). No data is shown as zero, which in some coastal spots looks a bit odd. When zooming in and finding a pixel with value zero close to the coast and with neighbouring pixels looking very suitable, then that pixel is probably a NO DATA pixel. Our apologies for this.</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityOverall_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0). Values outside the EEZ set to NULL</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityOverall_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0). Values outside the EEZ set to NULL</td> </tr> <tr> <th scope="row"> WorldEEZY_MonthSuitabilityOverall_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0). Values outside the EEZ set to NULL</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityAll_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months.</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityAll_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months.</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityAll_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing 7 simulated biophysical suitabilities (for all 6 environmental variables & overall suitability; range 0.0-1.0), for 12 months.</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityGt_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8).</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityGt_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8).</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityGt_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing simulated number of suitable months (range 0-12) at 4 thresholds for overall suitability thresholds (0.5, 0.6, 0.7, 0.8).</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityOverall_Cold.nc </th> <td>Netcdf file for the world, for the cold temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0).</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityOverall_Intermediate.nc </th> <td>Netcdf file for the world, for the intermediate temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0).</td> </tr> <tr> <th scope="row"> WorldY_MonthSuitabilityOverall_Warm.nc </th> <td>Netcdf file for the world, for the warm temperature seaweed species group. Showing for each of 12 months simulated overall suitability (range 0.0-1.0).</td> </tr> </tbody> </table>

openother-openAug 2023View details →

RAP-Chem Model Output for the Beckwourth Complex Fire

RAP-Chem data for the Beckwourth Complex Fire between 7/7/2021 and 7/14/2021.

Model output for the effects of topography and orbital forcing

modeling data for the Asia

AM4 Output Data for Clark, Lin, Hill (2023)

Data associated with the study entitled "ITCZ Response to Disabling Parameterized Convection in Global Fixed-SST Aquaplanet Simulations at 50 km and 6 km Resolutions," coauthored by Joseph P. Clark, Pu Lin and Spencer A. Hill

Basal Area Estimates for DBH Classes from ARRI Modelled Outputs

Summary Basal area per acre (BAA) for ‘All’ trees, ‘Pine’ trees, and ‘Non-Pine’ trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds. Description Three modelling method raster outputs of basal area per acre across three diameter at breast height (DBH) size classes are contained here. Single model raster outputs are linear regression models, ensemble linear models (LM) are mean estimates from 50 linear models, and ensemble generalized additive models (GAM) are mean estimates from 50 GAMs. The units for these single band rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus Pinus), and 'Non-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches. Ensemble generalized additive regression models (GAM) and Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The ensemble LM models were created using a modification of the Hogland, 2021 script that uses the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM, and LM, models for each cell were averaged to produce the mean estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell.     The 246 Restore field plots used to train the models of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems.  Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.   The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated basal area per acre as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel.     Reference Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a> St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114  Credits This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project. Use Limitations This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.

Reward expectation enhances action-related activity of nigral dopaminergic and two striatal output pathways

Data for making main and supplementary figures from 'Reward expectation enhances action-related activity of nigral dopaminergic and two striatal output pathways'. The figure (and the corresponding subplot) is specified in each file. In the behavior folder, each row corresponds to one recording session. In the neuronal activity data, each row correspond to one neuron. In the dLight data, each row corresponds to one recording session.

PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output, Jan 2010 through Sep 2021)

The dataset archived here includes an extended version of the analysis output shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide a single NetCDF file of the analysis output (9-km resolution EASEv2 grid, period Jan 2010 through Sep 2021, and between 45°N and 70°N, NE Asia excluded): •    daily_images.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack The file content is described in the file PEATCLSM_Tb_Documentation_20230830.pdf Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions. Data usage statement: This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/ If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks! References: Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. Journal of Advances in Modeling Earth Systems, 11(7), 2130–2162. https://doi.org/10.1029/2018MS001574 Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.111805 Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. Journal of Advances in Modeling Earth Systems, 11(10), 3106–3130. https://doi.org/10.1029/2019MS001729

openodc-pddlFeb 2023View details →

Quast outputs for "When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes"

Quast outputs for "When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes"

opencc-by-4.0Sep 2023View details →

AWI-CM3 output of mid-Holocene and pre-industrial experiments

This repository contains the AWI-CM3 output of the pre-industrial (PI) and mid-Holocene (MH) experiments. Files description 2t=> surface air temperature (2m) a_ice=> sea ice concentration radiation: radiation: components of Arctic surface energy budget PI and MH both contains 5 ensemble runs, which are labeled as 001-005. For more data details, please refer to the self-describing information of NetCDF files.

opencc-by-4.0Apr 2023View details →

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity"

Model outputs associated with "Comprehensive multiphase chlorine chemistry in the box model CAABA/MECCA: Implications to atmospheric oxidative capacity”.

opencc-by-4.0Sep 2023View details →

Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality".V2

A minor coding error was found in the pre-formatted data of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16630">Barrere et al. (2023)</a>. This error did not affect the main results of the paper, but led to minor change in the value of the posterior estimates, stored in data/sensitivity/jags_dominance.Rdata. This repository contains the new version of the parameters.

opencc-by-4.0Sep 2023View details →

CLIMBER-X time series output of simulated Dansgaard-Oeschger events

CLIMBER-X time series output of simulations showing Dansgaard-Oeschger -like events. The amoc_* files contain the time series of the maximum Atlantic overturning streamfunction for the simulations with different ice sheets and atmospheric CO2 concentrations as specified in the file name. The ocn_ts_* files contain the time series of the maximum Atlantic overturning streamfunction and the buoyancy flux integrated north of 55°N in the North Atlantic for the simulations with different ice sheets and gradually decreasing or increasing atmospheric CO2 concentration as specified in the file name. File names ending with *_noise.nc are for simulations with noise and those ending with *_nonoise.nc are for simulations without applied noise.

Output datasets from model for the paper "On the potential role of viscoelasticity in fluid-induced seismicity"

Output datasets from model for "On the potential role of viscoelasticity in fluid-induced seismicity"

Adaptation of sea turtles to climate warming: will phenological responses be sufficient to counteract changes in reproductive output?

README: Supplementary Information   In this document, we list the supplementary material that supports our results and conclusions and we provide a description of what each file contains.   Supplementary Tables   Supp. Info. Table S1: metadata This file contains various information about the nesting sites studied, including their location and respective Regional Management Unit (RMU, as per Wallace, B. P., et al. 2010. "Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales." PLoS One 5(12): e15465), the number of nesting seasons with nest count data and how monitoring is conducted, the number of temperature loggers deployed, their type and where they were placed in the clutch, hatchling measurements in terms of straight carapace length (SCL in mm), and whether data from the literature (incubation experiments at constant temperature) was available at the RMU level to estimate thermal tolerance curves (see the column labeled ‘hatching success lab. data for this RMU (literature)’) and sex ratio thermal reaction norms (see the column labeled ‘sex ratio lab. data for this RMU (literature)’). The file also summarizes the parameters used to reconstruct nest temperature following the method in Monsinjon, J. R., et al. (2019) "The climatic debt of loggerhead sea turtle populations in a warming world." Ecological Indicators 107: 105657: mean diel thermal amplitude in °C (daily maxima minus daily minima), average time of daily min. temperatures in decimal hours, average time of daily max. temperatures in decimal hours, number of days lagged with sea surface temperature (SST), number of days lagged with two-meter air temperature (T2M), GLM coefficient of the Intercept, GLM coefficient of the relationship with SST, GLM coefficient of the relationship with T2M, GLM coefficient of the relationship with the proportion of incubation time used to infer metabolic heating (MH), and standard deviation of the coefficients of the nests from the GLMM random effect used to estimate the thermal heterogeneity. IPCC regions from which predicted increases in air and sea temperatures were extracted are indicated along with the values extracted for the future changes in temperature (median). The settings selected to extract the warming scenarios from the online interface are given below: <ul> <li>IPCC's atlas: https://interactive-atlas.ipcc.ch/regional-information</li> <li>Dataset = CMIP6 (Model projections)</li> <li>Variable = Mean temperature (T) and Sea Surface Temperature (SST) anomalies (change in deg C)</li> <li>Region set = WGI reference-regions (or Small islands for Tetiaroa, French Polynesia)</li> <li>Uncertainty = Advanced</li> <li>Baseline period = 1981-2010</li> <li>Future period = 2081-2100</li> <li>Season = Annual</li> <li>Scenario “Middle of the road” (SSP2-4.5): Approximately in line with the upper end of combined pledges under the Paris Agreement. The scenario “deviates mildly from a ‘no-additional climate-policy’ reference scenario, resulting in a best-estimate warming around 2.7°C by the end of the 21st century”.</li> </ul> The remaining columns show the shift (number of days) in nesting phenology estimated for the IPCC regions according to seawater warming scenarios, and using either the mean or the extreme (max.) coefficient of the negative linear relationship between nesting dates and sea water temperature (using the literature data presented in Supp. Info. Table S6).   Supp. Info. Table S2: hatching success literature data This file contains the literature data on hatching success from incubation experiments conducted at various constant temperatures. For each species, the Regional Management Unit (RMU) is specified.   Supp. Info. Table S3: mean temperature and hatching success This file contains in-situ hatching success data and associated mean temperatures during the whole incubation period. Data are from the literature and the present study (refer to Supp. Info. Table S1 for the 3-letter beach codes). Note that the nesting season is specified only for the present study.   Supp. Info. Table S4: sex ratio literature data This file contains the literature data on sex ratio from incubation experiments conducted at various constant temperatures. For each species, the Regional Management Unit (RMU) is specified.   Supp. Info. Table S5: in situ hatching success This file contains in-situ hatching success data measured at 19 of our 24 study sites encompassing the four species considered for this study: Caretta caretta, Chelonia mydas, Eretmochelys imbricata, and Lepidochelys olivacea (refer to Supp. Info Table S1 for the 3-letter beach codes). When the number of eggs was not available, we calculated the number that hatched by multiplying the survival proportion by 100 and rounding the value. And we calculated the number of eggs that did not hatch by subtracting the number that hatched from 100.   Supp. Info. Table S6: phenological shifts This file contains the literature data on the relationship between nesting dates and thermal environmental cues. The grey rows (*) indicate cases that were not considered because the study reported either non-significant relationships or positive relationships between the proxy for nesting phenology and the environmental cue (i.e., a delay of nesting dates with increasing temperatures instead of a shift earlier as assumed in the present study).   Supp. Info. Table S7: required phenological shifts earlier and later in the nesting season This file contains the estimated required phenological shifts (number of days earlier or later in the season to stay within present-day conditions) and associated rates (number of days earlier or later per 1°C increase in sea surface temperature) that would be necessary to achieve required shifts. Rates were calculated by dividing the required shifts by projected increases in sea surface temperature at our sites (see Supp. Info. Table S1). Required shifts and rates were calculated for our indicators of incubation temperature (IT in column labels; phenological shifts required for the future median incubation temperature index to remain below the 75th percentile of current conditions), hatching success (HS in column labels; phenological shifts required for the future median hatching success index to remain above the 25th percentile of current conditions), and sex ratio (SR in column labels; phenological shifts required for the future median sex ratio index, in proportion of males, to remain above the 25th percentile of current conditions). NAs mean that no shift was found to remain within present-day conditions. Refer to Supp. Info Table S1 for the 3-letter beach codes.   Supp. Info. Table S8: hatching success and sex ratio ranges per site This file contains statistics (minimum, median, and maximum) on hatching success (HS in survival proportion) and sex ratio (SR in male proportion) for each study site, and the climate (SSP2-4.5) and phenology (no shift, mean shift, and max. shift) scenarios presented in the core manuscript. For each study site (see Supp. Info. Table S1 for the 3-letter beach codes in the first column), ranges are calculated between 2007-2020 for the present conditions and between 2059-2100 for the future conditions.   Supplementary Figures   Supp. Info. Figure S1: hatching success and sex ratio reaction norms This figure shows the hatching success and sex ratio thermal reaction norms estimated using literature data at controlled incubation temperatures (see data in Supp. Info. Table S2 and Supp. Info. Table S4) for the four species considered here: Caretta caretta, Chelonia mydas, Eretmochelys imbricata, and Lepidochelys olivacea. Points are observations and error bars are their confidence intervals. Continuous lines are estimated curves with shades of grey being the confidence intervals. Reaction norms were fitted using data at the species-level (black) and the RMU-level (red) when available.   Supp. Info. Figure S2: output examples in 2018-2020 This figure shows five successive panels for each study site. In the first panel, we show reconstructed nest temperature without metabolic heating under present (black) and future (SSP2-4.5 in red) scenarios. The blue line shows the estimated pattern of nesting activity assuming no change in phenology and the purple and pink ones corresponds to those assuming a maximum shift (i.e., -18.85 d.°C-1) and a mean shift (i.e., -6.86 d.°C-1), respectively, in nesting dates under the SSP2-4.5 warming scenario (see median values in Supp. Info. Table S1). The following panels show the outputs for hatching success and sex ratio under the present and SSP2-4.5 climate scenarios. Information on the study sites is shown in the title (species, location, 3-letter beach codes).   Supp. Info. Figure S3: required shift for incubation temperature This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median incubation temperature index (the black continuous line, with grey shaded areas representing the 25th and 75th percentiles) to remain below the 75th percentile of current conditions. The black rectangle represents the 25th and 75th percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to Supp. Info. Table S1 for the 3-letter beach codes.   Supp. Info. Figure S4: required shift for hatching success This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median hatching success index (the black continuous line, with grey shaded areas representing the 25th and 75th percentiles) to remain above the 25th percentile of current conditions. The black rectangle represents the 25th and 75th percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to Supp. Info. Table S1 for the 3-letter beach codes.   Supp. Info. Figure S5: required shift for sex ratio This figure shows the backward (earlier nesting) and forward (later nesting) phenological shifts required (vertical arrows) for the future median sex ratio index, in proportion of males (the black continuous line, with grey shaded areas representing the 25th and 75th percentiles), to remain above the 25th percentile of current conditions. The black rectangle represents the 25th and 75th percentiles of current conditions and the black point is the median. The red point shows the median of future conditions under the SSP2-4.5 warming scenario assuming no phenological shift, the blue point according to the mean phenological shift, and the green point according to the maximum phenological shift. Refer to Supp. Info. Table S1 for the 3-letter beach codes.   Supp. Info. Figure S6: incubation temperature fit quality for each site This figure shows the predicted vs observed daily mean incubation temperatures (individually for each site). The grey dashed line is the line of equality, and the red line shows the orthogonal regression. Refer to Supp. Info. Table S1 for the 3-letter beach codes.   Supp. Info. Figure S7: sensitivity analysis This figure shows the differences in hatching success (survival proportion) and sex ratio (male proportion) when predicted using laboratory data (from constant temperature experiments found in the literature: see Supp. Info. Table S2 and Supp. Info. Table S4) either at the species level or at the Regional Management Unit (RMU) level. Differences are plotted for two climate scenarios (present and SSP2-4.5) and three phenology scenarios (no shift, mean shift, max. shift: see values in number of days shifted earlier in Supp. Info. Table S1). We considered only predictions for Caretta caretta (Cc in orange), Eretmochelys imbricata (Ei in yellow) and Lepidochelys olivacea (Lo in green). Data at the RMU level were not available at our study sites for Chelonia mydas. Black dashed lines represent the line of equality.

Simulation output for manuscript Measuring tropical rainforest resilience under non-Gaussian disturbances

Open the record for dataset details and reuse information.

LarsAckermann GMD Iceberg 2023 - model output ICBHF

This dataset contains model output from the experiment ID "icbHF" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061---------------------------------------------------------It contains:mean output over the last 100 model years of:  - 3D ocean temperature  - 3D salinity  - 2D sea-ice height  - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins  - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basinstimeseries over 700 years of:  - global ocean temperature  - AABW for the IndoPacific sector  - global AABWyearly data of:  - 3D ocean temperature  - 3D vertical velocity  - 3D vertical bolus velocity  - 3D Väisälä frequency

LarsAckermann GMD Iceberg 2023 - model output ICBFW

This dataset contains model output from the experiment ID "icbFW" with the Earth system model AWI-ESM2.1 and interactive icebergs published in https://doi.org/10.5194/egusphere-2023-2061---------------------------------------------------------It contains:mean output over the last 100 model years of:  - 3D ocean temperature  - 3D salinity  - 2D sea-ice height  - zonal mean temperature for the Atlantic, Pacific and Indian Ocean basins  - zonal mean salinity for the Atlantic, Pacific and Indian Ocean basinstimeseries over 700 years of:  - global ocean temperature  - AABW for the IndoPacific sector  - global AABWyearly data of:  - 3D ocean temperature  - 3D vertical velocity  - 3D vertical bolus velocity  - 3D Väisälä frequency