Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Data, scripts, and figures of the article: The effect of oregano essential oils on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model

This data set contains the data, JMP scripts, and figures of the article titled "The effect of oregano essential oil on Feed Passage Syndrome in broilers: 2. Assessment under a challenge model" to be published in the journal Animal - Open Space.

opencc-by-4.0Feb 2023View details →

Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models

These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows: - two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet) - two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution) - two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014) - two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)

opencc-by-4.0Apr 2023View details →

Model run and scenario data for study "Bioenergy-induced land-use change emissions with sectorally fragmented policies"

This data archive contains model runs and data analysis files to the research article Bioenergy-induced land-use change emissions with sectorally fragmented policies by Leon Merfort, Nico Bauer, Florian Humpenöder, David Klein, Jessica Strefler, Alexander Popp, Gunnar Luderer, Elmar Kriegler published in Nature Climate Change (2023). ModelRuns_remind (directory) contains all REMIND model runs of the scenarios underlying the paper. ModelRuns_magpie (directory) contains all MAgPIE model runs of the scenarios underlying the paper. DataAnalysis (directory) contains an RStudio Project that was used for the data analysis and the generation of the figures of the paper. It additionally contains all figures and figure data that are shown in the paper. ScenarioMapping.pdf  contains the mapping from scenario names used in the paper to the model experiment names (in the model run directories).

opencc-by-4.0Apr 2023View details →

Building measured data for model validation

Measured indoor/outdoor temperatures, solar radiation and heating load of a 103-m2 building in Athens, Greece. Data include measurements of two weeks, one without heating delivery to the building and another with heating delivery (with fan coils).

opencc-by-4.0Apr 2023View details →

Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox

Description: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI. Use these data: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script g20230501_FA_Artificial_listener_paper_figs.m (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function publ_osses2023b_FA_figs.m from the toolbox.

TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling

Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

BTO Garden BirdWatch: Weekly butterfly abundance data for modelling trends in UK gardens

Dataset used to estimate annual abundance indices and trends for UK butterflies in gardens, covering the period 2007 to 2020. Data have been collected as part of the British Trust for Ornithology (BTO) Garden BirdWatch (GBW) survey. GBW is a structured, citizen science monitoring programme whereby volunteers record weekly abundances of various bird, invertebrate, mammal, reptile  and amphibian species in (predominantly suburban and rural) gardens. See <a href="http://www.bto.org/gbw">www.bto.org/gbw</a> for further information about the survey.  This dataset has been pre-filtered to meet criteria for inclusion in the modelling of butterfly species trends, as described by <a href="https://doi.org/10.1111/icad.12645">Plummer et al 2023</a>. Please refer to the 'readme' file for further details. We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!

Numerical convergence of model Cauchy-Characteristic Extraction and Matching (data)

This dataset was used to produce the convergence plots in the paper "Numerical convergence of model Cauchy-Characteristic Extraction and Matching", as well as additional convergence tests that can be found in the repository https://github.com/ThanasisGiannakopoulos/model_CCE_CCM_public. The data can be used to reproduce the aforementioned convergence plots or for comparison against data obtained if one performs the same simulations independently.

opencc-by-4.0Jun 2023View details →

Lake Mendota metabolism model and data set

This is a zipped file that includes the model code, written in R, as well as the input and output data for the model. This publication accompanies the manuscript entitled, Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake

Demo data for global-canopy-height-model

Demo data for the example scripts provided in <a href="https://github.com/langnico/global-canopy-height-model">https://github.com/langnico/global-canopy-height-model</a>.Please see the README in the github repository for further information and see Lang, et al. (2023) for more information.Reference:Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a>

opencc-by-4.0Jan 2023View details →

Open-population models for estimating roadkill rates - Data and R Code

Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.

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>

AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea

The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria.

Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"

This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper: "Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

Data from: Calculating global annual methane increases from satellite data using an ensemble dynamic linear model approach

NOTE: This is no official S5P/TROPOMI WFMD XCH4 L3-Dataset. This data is used and created by the example code provided in <a href="http://www.doi.org/10.5281/zenodo.8178927">10.5281/zenodo.8178927</a>, which is a supplement to the manuscript 'Zonal variability of methane trends derived from satellite data' (Hachmeister et al., 2024 ; 10.5194/acp-24-577-2024). This data can be downloaded to skip the gridding step in the mentioned example code, to avoid downloading the complete input data.

FESOM-REcoM model data: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario

This data set includes the minimal data necessary to reproduce the findings of Nissen et al. (2023). Output of model simulations with the global ocean biogeochemical model FESOM1.4-REcoM2 is provided. In particular, besides information on the model grid, the data set includes annual mean water mass properties (temperature, salinity, density, oxygen, pH) and freshwater fluxes from sea ice and ice shelves and decadal averages of air-sea CO2 fluxes and deep-ocean carbon accumulation rates. Model results are provided from 1980-2100 for the four emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (sorted from low emission to high emission). Please see README for more information on the individual files.  Data set belongs to:  Nissen, C., R. Timmermann, M. Hoppema, and J. Hauck, 2023: A regime shift on Weddell Sea continental shelves with local and remote physical-biogeochemical implications is avoidable in a 2°C scenario. J. Climate, <a href="https://doi.org/10.1175/JCLI-D-22-0926.1">https://doi.org/10.1175/JCLI-D-22-0926.1</a>, in press.