Find research datasets worth reusing

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula

The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”. The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncJun 2022View details →

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast

The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay

The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling  was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea

The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea

The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.   Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea

The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

opencc-by-4.0Apr 2024View details →

Cordilleran ice sheet improved bedrock simulations continuous variables

These data contain a subset of time-dependent glacier model output variables. The ghf70 data files are an update on the reference below, fixing significant problems affecting the computation of the bedrock deformation in response to ice load (PISM Github issues <a href="https://github.com/pism/pism/issues/370">#370</a> and <a href="https://github.com/pism/pism/issues/377">#377</a>) and the computation of ice temperature (PISM Github issue <a href="https://github.com/pism/pism/issues/371">#371</a>). The other data files additionally include spatially-variable geothermal heat flux (dav13, gou11comb, gou11simi, sha04), different lithospheric rigidity (eet30km) or mantle viscosity (num1e21), and higher horizontal resolution (3km). Reference: <ul> <li>Seguinot, J., Rogozhina, I., Stroeven, A. P.,  Margold, M. and Kleman, J.: Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, The Cryosphere, 10(2), 639–664, doi:<a href="https://doi.org/10.5194/tc-10-639-2016">10.5194/tc-10-639-2016</a>, 2016.</li> </ul> File names: <code>cisbed.{res}.{forcing}.{ex.100a|ts.10a}.{ghf}.{props}.nc</code> <ul> <li>Horizontal resolution: <ul> <li> 10km: 10 km horizontal resolution</li> <li>5km: 5 km horizontal resolution</li> <li>3km: 3 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li>epica: EPICA ice core temperature forcing</li> <li>grip: GRIP ice core temperature forcing</li> </ul> </li> <li>Variable types: <ul> <li>ex.100a: spatial diagnostics every hundred years</li> <li>ts.10a: scalar time-series every ten years</li> </ul> </li> <li>Geothermal heat flow: <ul> <li>ghf70: constant 70 mW m-2 heat flow</li> <li>dav13: Davies (2013) geothermal heat flow map</li> <li>gou11comb: Goutorbe et al. (2011) best combination method</li> <li>gou11simi: Goutorbe et al. (2011) similarity method</li> <li>sha04: Shapiro and Ritzwoller (2004) heat flow map</li> </ul> </li> <li>Bedrock properties <ul> <li>eet30km: lithosphere elastic thickness of 30 km</li> <li>num1e21: astenosphere viscosity of 1e21 Pa s</li> </ul> </li> </ul> Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (*.ex.100a.nc) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL: <code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code> The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. Band information can be displayed with: <code>gdalinfo NETCDF:filename.nc:variable</code> Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Funding: Swiss National Supercomputing Centre (CSCS) grants s573 and sm13 to J. Seguinot, Swiss National Science Foundation (SNSF) grants no.~200020-169558 and 200021-153179/1 to M. Funk, and Research Foundation – Flanders (FWO) Odysseus Type II project G0DCA23N 'GlaciersMD' to H. Zekollari. Changelog: <ul> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>

opencc-by-4.0Sep 2023View details →

Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons

We present here the raw data and scripts to reproduce the results presented in the preprint "Disentangling the effects of eutrophication and natural variability on macrobenthic communities across French coastal lagoons" available on BioRxiv. Before using the scripts and associated data, we recommend reading the "readme" word document also available, which details the information available in the different data sheets.  Preprint abstract :  Coastal lagoons are transitional ecosystems that host a unique diversity of species and support many ecosystem services. Owing to their position at the interface between land and sea, they are also subject to increasing human impacts, which alter their ecological functioning. Because coastal lagoons are naturally highly variable in their environmental conditions, disentangling the effects of anthropogenic disturbances like eutrophication from those of natural variability is a challenging, yet necessary issue to address. Here, we analyze a dataset composed of macrobenthic invertebrate abundances and environmental variables (hydro-morphology, water, sediment and macrophytes) gathered across 29 Mediterranean coastal lagoons located in France, to characterize the main drivers of community composition and structure. Using correlograms, linear models and variance partitioning, we found that lagoon hydro-morphology (connection to the sea and lagoon surface), which affects the level of environmental variability (salinity and temperature), as well as lagoon-scale benthic habitat diversity (using macrophyte morphotypes) seemed to regulate macrofauna distribution, while eutrophication and associated stressors like low dissolved oxygen, acted upon the existing communities, mainly by reducing species richness and diversity. Furthermore, M-AMBI, a multivariate index composed of species richness, Shannon diversity and AMBI (AZTI's Marine Biotic Index) and currently used to evaluate the ecological state of French coastal lagoons, was more sensitive to eutrophication (18%) than to natural variability (9%), with nonetheless 49% of its variability explained jointly by both. To improve the robustness of benthic indicators like M-AMBI and increase the effectiveness of lagoon benthic habitat management, we call for a revision of the ecological groups at the base of the AMBI index and of the current lagoon typology which could be inspired by the lagoon-sea connection levels used in this study.

opencc-by-4.0Apr 2023View details →

Data from: Enamel proteins reveal biological sex and genetic variability within southern African Paranthropus

This dataset contains the sequences of Paranthropus robustus, first described in 'Enamel proteins reveal biological sex and genetic variability within southern African Paranthropus', as well as the reference data and all the results from the analysis of those sequences. Folders and Sub-Folders: - Paranthropus_Raw_AA_Sequences_Unaligned: Contains 2 fasta files. Paranthropus_Unaligned.fasta contains all the Paranthropus robustus sequences that were used for all of the analyses. Paranthropus_Unaligned_UNFILTERED.fasta contains all the Paranthropus robusts sequences before filtering for SAP quality/confidence. These sequences were not used in any of the analyses, but are provided here for openness.      - Reference_Datasets: Contains 3 fasta files. Each fasta file is a reference dataset used in at least one analysis. The identity and origin of each sample is described in the supplementary document of the publication.     - Phylogenetic_Analysis_Datasets_and_Trees: Contains the following five folders     - Paranthropus_Alignments_All_Datasets: Contains three folders. Each folder contains the aligned and I/L corrected MSAs (Multiple Sequence Alignments) of Paranthropus robustus and a reference dataset.     - Paranthropus_Diversity_Dataset_Trees_Results: Contains all analysis done using the 'diversity' reference dataset. Contains one folder for each protein, which includes the protein alignment and the phylogenetic tree of that protein. Additionally a folder named 'CONCATENATED' contains the concatenated alignemnts and trees. The BEAST2-STARBEAST3 folder contains the Starbeast3 analysis, including the xml, output log file, output trees and the input taxon set file.     - Paranthropus_Representative_Dataset_Trees_Results: Contains all analysis done using the 'representative' reference dataset. Contains one folder for each protein, which includes the protein alignment and the phylogenetic tree of that protein. Additionally a folder named 'CONCATENATED' contains the concatenated alignemnts and trees. The BEAST2 folder contains the time-calibrated BEAST2 analysis, including the xml, output log file, output trees. The folder Distance_Matrix contains the generated distance matrix and the Rscript used to generate the heatmap from it.     - Paranthropus_Independent_Dataset_Trees_Results: Contains all nexus files and tree-figures used in the analysis of the 'independent' reference dataset.     - Tree_Figures: Contains three sub-folders and an additional figure. Each sub-folder contains the phylogenetic tree figures generated using one of the three reference datasets.

opencc-by-4.0Oct 2021View details →

Transparent Exopolymer Particles (TEP), Coomassie Stainable Particles (CSP) and accompanying variables in seawater of the NW Mediterranean

Concentrations of Transparent Exopolymer Particles (TEP) , Coomassie Stainable Particles (CSP), nitrate, silicate, phosphate, chlorophyll a (chla), particulate organic carbon (POC) and nitrogen (PON), seawater temperature, salinity, water transparency, solar radiation, flow cytometry-determined abundances of  Prochlorococcus, Synechococcus, picoeukaryotes, nanoeukaryotes, prokaryotic heterotrophs (PHA), and microscopy-detrrmined abundances of dinoflagellates, diatoms, coccolithophores and other microalgae in the NW Mediterranean. Sampling sites are the Blanes Bay Microbial Observatory (monthly sampling between 22/06/2015 and 10/10/2017) and the L'Estartit Oceanographic Station (monthly sampling between 25/06/2015 and 11/10/2017) in the coastal NW Mediterranean, plus the MIFASOL cruise (October 2015) onboard the RV Garcia del Cid in the NW Mediterranean.

Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power

This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.

Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis

Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to Podarcis muralis species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a>

Dataset for "IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean"

Dataset for Sperlich, P., Brailsford, G. W., Moss, R. C., McGregor, J., Martin, R. J., Nichol, S., Mikaloff-Fletcher, S., Bukosa, B., Mandic, M., Schipper, I., Krummel, P. and Griffiths, A. D.: IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean, Atmos. Meas. Tech., https://doi.org/10.5194/amt-15-1-2022, 2022.

Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) Dataset of a study with 5 real-world large numerical variability models.

The publications and research associated to cite is in:<a href="https://doi.org/10.1016/j.knosys.2023.110558">https://doi.org/10.1016/j.knosys.2023.110558</a>In that research we detail the Smart Analyser of Variability Requirements of Unknown Spaces (SAVRUS) approach, and provide a web-tool prototype in <a href="https://hadas.caosd.lcc.uma.es/savrus">https://hadas.caosd.lcc.uma.es/savrus</a>In the study, we model 5 different real-world software product lines to then analysed them with SAVRUS:Detailed real-world variability models ordered by their search space size, of which GEC QA is incompletely measured NVM Description #Booleans #Numericals Space QA #Measurements Dune1 Multi-grid solver 11 3 2,304 Complex.. 2,304 HSMGP1 Stencil-grid solver 14 3 3,456 ..equation.. 3,456 HiPAcc1 Image processing framework 33 2 13,485 ..solving.. 13,485 Trimesh2 Triangle mesh library 13 4 239,360 ..time 239,360 GEC Generic edge computing 552 2 ~5.3*108 Energy Consumption 132500 The dataset zip file contains:<ul><li>5 numerical variability models in Clafer format (.txt) for each software product line.</li><li>5 CSV files with the respective quality attribute measurements</li><li>An .xlsx file containing SAVRUS scalability results divided in different tabs.</li></ul>References:[1] N. Siegmund, A. Grebhahn, S. Apel, C. Kastner, Performance-influence models for highly configurable systems, in: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, ESEC/FSE 2015, Association for Computing Machinery, New York, NY, USA, 2015, p.284–294. doi:10.1145/2786805.2786845.[2] M. Bauer, A comparison of six constraint solvers for variability analysis, Tech. rep., University of Passau (2019).

Within Population Variability of Coral Heat Tolerance - Images

Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164.

A database of physical therapy exercises with variability of execution collected by wearable sensors

The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function features_extraction, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.

opencc-by-4.0Nov 2021View details →

Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis

Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques.  Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882 Changelog: V 1.0 - Initial version. V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.

opencc-by-4.0Apr 2022View details →

The effects of solar cycle variability on nanodust dynamics in the inner heliosphere: Predictions for future STEREO A/WAVES measurements

This dataset contains results from the associated manuscript in JGR Space Physics. The dataset consists of two-dimensional nanodust grain fluxes in the HEEQ equatorial plane for various specified Carrington Rotations (CRs), as specified in the parent manuscript.

Collective Variable for Metadynamics Derived from AlphaFold Output

AlphaFold is the state of the art method for prediction of 3D structures of proteins from the amino acid sequence by neural networks. One of the outputs of AlphaFold is a probability profile of inter-residue distances for all residue pairs. We used this profile to evaluate any conformation of the studied protein to express its compliance with the AlphaFold prediction. This value can be used as a collective variable in metadynamics or parallel tempering metadynamics to accelerate protein folding in a molecular simulation. We applied this approach on folding of mini-proteins Trp-cage and beta hairpin. See V. Spiwok, M. Krečka & A. Křenek: <a href="http://doi.org/10.3389/fmolb.2022.878133">Collective Variable for Metadynamics Derived from AlphaFold Output</a> Frontiers in Molecular Biosciences 9 878133 (2022) DOI: 10.3389/fmolb.2022.878133.