Find research datasets worth reusing

Heterogeneous environmental seascape across a biogeographic break influences the thermal physiology and tolerances to ocean acidification in an ecosystem engineer

Dataset for the metabolic rates of limpets under two different pCO2/pH conditions MR are in O2 mg h−1g−1

Paleomagnetic Evidence of the Deformation of the Pontides during the closure of the Intra-Pontide Ocean in the Early Cretaceous

Several models exist concerning the deformation history of the Pontides in North Anatolia during the Cretaceous period, which vary depending on the positions of the Istanbul and Sakarya zones, the consumption of the northern branches of the Neotethys ocean and the rifting of several sub-basins. Notably, the early Cretaceous tectonic history of the Pontides involved the closure of the northern Neotethys ocean (Intra-Pontide ocean), and the collision between the Istanbul and Sakarya zones, producing thrust structures along the collisional front. The lack of paleomagnetic data providing evidence for this deformation pattern demonstrates that further investigation is required, particularly focusing on the Lower Cretaceous strata in the Pontides. Thus, the present study aimed to examine samples from a total of 78 sites from the Lower-Upper Cretaceous sedimentary rocks, and Middle Eocene to Middle Miocene sedimentary and volcanic rocks. Results of the present study indicated large counter-clockwise rotations up to R±DR=-73.9°±9.1°, and small clockwise rotations of R±DR= 14.2°±12.2° in the Istanbul and Sakarya zones, during the Early Cretaceous and Late Cretaceous periods. These rotation patterns are accompanied by the closure of the Intra-Pontide ocean, and the collision between the Istanbul and Sakarya zones during the Early and Late Cretaceous periods. On the other hand, in the Middle Eocene, small counter-clockwise rotations of R±DR=-6.4°±13.9° and R±DR=4.6°±12.9° along the western coastline of the Pontides indicated that the northern margin of the Pontides was stable during this period.

opencc-by-4.0Apr 2023View details →

Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign

We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.

opencc-by-4.0Apr 2023View details →

Wordlist files of lexical data from Papua New Guinea and western Solomons Oceanic languages collated for Ross's 1986 PhD thesis and 1988 publication thereof

It occurs to me that the files containing Western Oceanic lexical data that I collected in the late 70s/early 80s for my PhD (Ross 1988) might be useful to someone. They are also used in the volumes of The lexicon of Proto Oceanic (Ross, Pawley & Osmond 1998, 2003, 2011, 2016, 2023). In any case, it is right that they be made publicly available, something that wasn't so easy back then. Most of the material is from wordlists that I collected during fieldwork in Papua New Guinea from around 1978 to 1982. The file cor06 is omitted because it contains SE Solomonic data (outside Western Oceanic) drawn from Tryon & Hackman 1983. I keyed the data into text files in a format such that each line was the entry for a single word, and each field within an entry was marked by a backslash code (I adapted this format from SIL's conventions at the time), then arranged them in cognate sets, each set separated from the next by an empty line. This work was done between 1983 and 1985, when text files were the best way to store data. They were entered on a terminal connected to a mainframe computer at the ANU. I have converted the ASCII symbols used in the original files into UTF-8 here in the interests of readability. The conversion was largely automatic, and I have not done a full check of each file, so there may be glitches. Each file contains languages from a region, as listed below (and the regions sometimes cut across subgroups determined by the comparative method). Three-letter abbreviations are used for language names, and two key files are also provided, one (COR-abbrevs) ordered by regions (determined by the numerals that start each line), the other by alphabetical order of language name (COR-abbrevs-alph). Some three-letter codes are followed by a hyphen and an extra letter. These are dialects. For example, MUM stands for Mumeng and MUM-P for the Patep dialect of Mumeng. Data files are labelled with COR (for 'correspondence sets') plus a numeral. The numerals are: 1-3 New Ireland; 4 Willaumez Peninsula (New Britain) area; 5 NW Solomonic; 7+8 Papuan Tip; 9 Vitiaz Strait area and NG north coast; 10 Huon Gulf and Markham Valley; 11 South and west New Britain. 7+8 are partial only. When I keyed the files, I had to rely on a mainframe's nightly back-up onto tape spools. One night the system failed, and so did the restore, and I lost some data. The backslash codes in the data files are: \l language; \p protolanguage; \w word; \g gloss; \n note; \s source. The formatting of these files is a little odd, since they served as input to routines I wrote to pull out sound correspondences. Anything after '%' is the elicited form: what immediately precedes '%' has had something 'undone', e.g. metathesis. The orthography of the files is phonemic and largely obvious. The conventions are set out in the introductions to the volumes of The lexicon of Proto Oceanic. Finally, the files also contain reconstructions at various interstages at the top of a cognate set. These were inserted for heuristic reasons during my research. Many of them did not survive into my PhD thesis, and they should preferably be ignored. The reader who is interested in current Oceanic reconstructions should turn to the volumes of The lexicon of Proto Oceanic.

opencc-by-4.0May 2023View details →

CLDF dataset derived from Greenhill et al.'s "Austronesian Basic Vocabulary Database" from 2022 focusing on Oceanic languages

Cite the source of the dataset as: <blockquote> Greenhill, S.J., Blust. R, & Gray, R.D. (2008). The Austronesian Basic Vocabulary Database: From Bioinformatics to Lexomics. Evolutionary Bioinformatics, 4:271-283. </blockquote>

opencc-by-4.0May 2023View details →

Marine heatwave datasheet for Northern Indian Ocean

The datasheet gives a detailed information on the marine heatwave intensity from 1981 to 2020 at the three coral reef regions (Andaman and Nicobar, Gulf of Mannar and Lakshadweep archipelago)  in the Northern Indian Ocean. This dataset was used to study various regional ecosystem changes from the variability in MHW over the period of time.

opencc-by-4.0Jun 2023View details →

Fisheries independent trawl survey data of fish biomass on North American and European oceanic shelves.

Publicly available scientific bottom trawl survey data, primarily sampling demersal commercial species, were obtained from the Northeast Pacific and North Atlantic shelf regions in 2021. The final dataset contains approx. 197,000 unique tows and includes data from 1970 to 2019 (166,000 tows between 1980-2015). For each tow in each survey, we selected all teleost and elasmobranch species and obtained species weight. We corrected these weights for differences in sampling area (in km2) and trawl gear catchability. The data processing scripts and individual survey data can be found on Github (DOI: 10.5281/zenodo.7992482). The data processing scripts are modified based on earlier work from Pinsky et al. (2013) and Maureaud et al. (2019). If the correction for gear catchability is not important, it is recommended to use the FishGlob database (DOI: 10.5281/zenodo.7484547). Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for questions. Column names Haul_id: unique haul identifyer Survey_Region: survey name or name of ecoregion (depending on survey) Gear: gear information (only included for northeast Atlantic region). Gear information is available for other regions. See original survey description (sources in manuscript). Year: sampling year Month: sampling month Longitude: longitude (EPSG:4326) Latitude: latitude (EPSG:4326) Swept_area: estimate of swept area of survey gear (only included for northeast Atlantic region) Bottom_depth: bottom depth in meters (as recorded in the survey data) Family: taxonomic family of the teleost/elasmobranch Name: species name (or higher taxonomic grouping) kg_km2: wet weight (kilogram) per unit of swept area (km2) kg_km2_corrected: wet weight (kilogram) per unit of swept area (km2) corrected for trawl gear catchability F_type: fish type (demersal or pelagic) Trophic_lev: Species-specific trophic level information Data uncertainties Data have predominantly been analysed at the community level and between 1980 and 2015. Any species-specific inferences may need further checking. To reduce the effect of potential outlying biomass estimates, it is recommended to remove all individual observations 1.5 times less/greater than the interquantile range per survey and year based on log10-transformed biomass values. Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for any comments/questions.

opencc-by-4.0Jun 2023View details →

Processed data supporting figures in Yang et al. 2023: Oceanic eddies induce a rapid formation of an internal wave continuum

This data repository supports a manuscript by Luwei Yang, Roy Barkan, Kaushik Srinivasan, James C. McWilliams, Callum J. Shakespeare, and Angus H. Gibson, submitted to Communications Earth & Environment. This repository contains the processed data that support the figures in the manuscript.

opencc-by-4.0Jul 2023View details →

Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"

Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast" includes:  The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu) Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed. The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is  The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively. The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively. the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a> the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a> the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a>

opencc-by-4.0Jul 2023View details →

Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method

Supporting data for Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method Matlab Codes to reconstruct the subsurface structures (Codes without Figure_*.m) and plot the figures (Figure_*.m) in the manuscript. The file in Netcdf format is our reconstructed 3D density and currents.

The Antarctic sea ice reconstruction (CMST-South) based on the optimized Data Assimilation System for the Southern Ocean

The wealth of historical sea ice concentration (SIC) observations, coupled with their extensive spatial coverage, renders them indispensable for the reconstruction of long-term Antarctic sea ice variability. However, recent studies have pointed out the presence of significant uncertainties in certain aspects of Antarctic sea ice reanalyses obtained from assimilating SIC. Notably, while previous studies on ocean data assimilation have already demonstrated the significance of optimizing model-dependent parameters for assimilating oceanic observations, this aspect has received limited attention in current sea ice data assimilation studies. As a result, whether optimizing model-dependent parameters can enhance the effectiveness of assimilating SIC remains an open question. Thus, we address this gap by refining the model-dependent parameters of Data Assimilation System for the Southern Ocean (DASSO), including the development of a latitude-dependent localization scheme and the objective estimation of observation error variance of SIC which takes into account both measurement errors and representation errors. Here, the monthly anomalies in Antarctic sea ice extent and volume (1980 -2018) are uploaded which is produced by the optimized Data Assimilation System for the Southern Ocean (DASSO) with assimilating SIC. Besides, a 13-month moving mean is applied to monthly anomalies to focus on the low-frequency variability of Antarctic sea ice.

nextGEMS: Output of the WP6 ocean vertical mixing sensitivity runs (timeseries)

In work package 6 of the nextGEMS project, several ocean-only model runs were performed with FESOM (Version 2.0) and ICON-O (Version 2.6.6), to test the sensitivity of the upper tropical Atlantic to different settings of the vertical mixing scheme. Two different mixing schemes were tested: TKE and KPP. For TKE, we tested different settings of the c_k parameter (0.1, 0.2 and 0.3), and for KPP different settings of the critical bulk Richardson number (0.3 and 0.27). These runs were done with both ICON-O and FESOM, to enable a comparison of the effects of the vertical mixing settings across different models. From ICON-O only, there are some additional TKE runs available, where we increased the interior ocean background mixing, and switched on the Langmuir turbulence parameterisation. There is also an ICON-O run which uses the FESOM default forcing bulk formulae, to check how much of the differences between the models originates from their different default bulk formulae. All model runs are ocean only, forced with hourly ERA5 reanalysis data. The horizontal resolution is 10km (for FESOM, the extratropical regions have a coarser grid). Here we provide high-frequency (3 hourly) time series of the model output, at selected locations in the tropical Atlantic where observational data are available for comparison in the simulated time range (2014 and 2015). We provide the following locations here: <ul> <li>0N, 10W</li> <li>0N, 23W</li> <li>11.5N, 23W</li> <li>15N, 38W</li> <li>11N, 21.2W</li> <li>17.6N, 24.3W</li> </ul>

ADCP Data from the Chukchi Sea, Arctic Ocean, acquired during the R/V Marcus G. Langseth expedition MGL1112

This is a processed acoustic Doppler current profiler (ADCP) dataset, provided by Andrew Frambach and Jules Hummon, acquired in September 2011 during the MGL1112 cruise at Chukchi Borderland, Arctic Ocean.

Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases

These data were used for the development of the paper "Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases". Especifically, we added ecological and environmental data that were used for modeling.

Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)

The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.

Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3

This is the underlying dataset of the publication "Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3" by Gürses et al. (in press), Geoscientific Model Development. In addition to unstructured mesh information, it contains the results of ocean biogeochemistry in the Regulated Ecosystem Model version 3 (REcoM3) coupled to the ocean and sea ice model FESOM2.1. The model simulations cover the period 1958 to 2021 and are forced with observed atmospheric CO2 and JRA55-do atmospheric reanalyses. Three simulations are provided: simulation A: with varying climate forcing conditions and varying atmospheric CO2 simulation B: with constant climate forcing conditions and constant atmospheric CO2 simulation D: with varying climate forcing conditions and constant atmospheric CO2 The following 2D/3D monthly-averaged fields (data period is given in parentheses) are provided on the native model grid: <ul> <li>Alk: Alkalinity (2012-2021)</li> <li>CO2f: Air-Sea CO2 flux (1800-2021)</li> <li>DFe: Dissolved Iron concentration (2012-2021)</li> <li>DIN: Dissolved Inorganic Nitrogen concentration (2012-2021)</li> <li>DIC: Dissolved Inorganic Carbon concentration (1800, 1994-2021)</li> <li>DSi: Dissolved Inorganic Silicon concentration (2012-2021)</li> <li>DiaChl: Chlorophyll a concentration of diatoms  (2012-2021)</li> <li>DetC: Carbon concentration in slow-sinking detritus (2012-2021)</li> <li>DetCalc: Calcite concentration in slow-sinking detritus (2012-2021)</li> <li>DetSi: Silicon concentration in slow-sinking detritus (2012-2021)</li> <li>idetz2c: Carbon concentration in fast-sinking detritus (2012-2021)</li> <li>idetz2calc: Calcite concentration in fast-sinking detritus (2012-2021)</li> <li>idetz2si: Silicon concentration in fast-sinking detritus (2012-2021)</li> <li>HetC: Small zooplankton carbon biomass (2012-2021)</li> <li>MLD: Mixed Layer Depth (2012-2021)</li> <li>NPPn: Net Primary Production of small pyhtoplankton (2012-2021)</li> <li>NPPd: Net Primary Production of diatoms (2012-2021)</li> <li>O2: Dissolved Oxygen concentration (2012-2021)</li> <li>PhyChl: Chlorophyll a concentration of small phytoplankton (2012-2021)</li> <li>Zoo2C: Macrozooplankton carbon biomass (2012-2021)</li> <li>pCO2s: Partial pressure of carbon dioxide of the surface ocean (1970-2021)</li> <li>salt: Salinity (2012-2021)</li> <li>temp: Temperature (2012-2021)</li> <li>w: Vertical velocity (2012-2021)</li> </ul> Please contact the corresponding author (ozgur.gurses@awi.de) for further information.

Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.

Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.

SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm

This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean" SST_FRONT_data.zip contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD).  SST_FRONT_data.zip also contains Fdens_Ffreq_Fstre_example.mlx, which is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre).  Fdens_Ffreq_Fstre_example.pdf is intended for quick viewing of the script above.

opencc-by-4.0Jan 2023View details →

Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters

The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications, 2023.

Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)

Model output of the global eddy-rich configuration used in the Geoscientific Model Development publication: "Evaluation of the CMCC global eddying ocean model for the Ocean Model Intercomparison Project (OMIP2)"  Abstract: This paper describes the global eddying ocean-sea ice simulation produced at the Euro-Mediterranean Center on Climate Change (CMCC) obtained following the experimental design of the Ocean Model Intercomparison Project phase 2 (OMIP2). The eddy-rich model is based on the NEMOv3.6 framework, with a global horizontal resolution of 1/16° and 98 vertical levels, and was originally designed for an operational short-term ocean forecasting system. Here, it is driven by one multi-decadal cycle of the prescribed JRA55-do atmospheric reanalysis and runoff dataset in order to perform a long-term benchmarking experiment. To access the accuracy of simulated 3D ocean fields, and highlight the relative benefits of mesoscale activities, the GLOB16 performances are evaluated via a selection of key climate metrics against observational datasets and two other NEMO configurations at lower resolutions: an eddy-permitting resolution (ORCA025) and a non-eddying resolution (ORCA1) designed to form the ocean-sea ice component of the fully coupled CMCC climate model.  The well-known biases in the low-resolution simulations are significantly improved in the high-resolution model. The evolution and spatial pattern of large-scale features (such as sea surface temperature biases and winter mixed layer structure) in GLOB16 are generally better reproduced, and the large-scale circulation is remarkably improved compared to the low-resolution oceans. We find that eddying resolution is an advantage in resolving the structure of western boundary currents, the overturning cells, and flow through key passages. GLOB16 might be an appropriate tool for ocean climate modeling effort, even though the benefit of eddying resolution does not provide unambiguous advances for all ocean variables in all regions.