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Figure 8 from: Ossowska E, Guzow-Krzemińska B, Kolanowska M, Szczepańska K, Kukwa M (2019) Morphology and secondary chemistry in species recognition of Parmelia omphalodes group – evidence from molecular data with notes on the ecological niche modelling and genetic variability of photobionts. MycoKeys 61: 39-74. https://doi.org/10.3897/mycokeys.61.38175
Figure 8 Distribution of suitable niches of P. omphalodes (A) and P. pinnatifida (B) in America.
Data Publication accompanying the paper "Methods to Evaluate Lifecycle Models for Research Data Management"
<p>The publications listed in dlc.bib were collected in 2017 and analysed.<br> The xml representations can be found in raw<br> dlc.csv includes the data summary.<br> </p>
Numerical simulations of the geospace response to a perfect interplanetary coronal mass ejection: Model Data
<p>This is the model output data discussed in the article, "Numerical simulations of the geospace response to a perfect interplanetary coronal mass ejection." See README.txt for information on dataset contents, formats, and suggested software libraries.</p> <p> </p>
Synthetic Data Set for Uplift Modeling
<p>This dataset is designed and simulated for evaluating uplift modeling and feature selection methods. The main feature of this dataset is that it generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</p> <p>This dataset consists of 100 trials (replicates with different random seeds), each trial with 10,000 samples and 36 features. The outcome variable is binary, that makes this dataset for classification problem. The samples are equally split for control and treatment group (5,000 samples in each group in each trial).</p> <p>The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To model the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine.</p> <p>In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features.</p> <p>Column names:</p> <ul> <li>Trial ID: 'trial_id'</li> <li>Experiment group label: 'treatment_group_key'</li> <li>Outcome variable (classification label): 'conversion'</li> <li>Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']</li> <li>True underlying control conversion probability: 'control_conversion_prob'</li> <li>True underlying treatment conversion probability: 'treatment1_conversion_prob'</li> <li>True treatment effect: 'treatment1_true_effect'</li> </ul>
'Learning the production cross sections of the Inert Doublet Model' training data set.
<p>Training data set used in the ''Learning the production cross sections of the Inert Doublet Model'' subproject, made of 50000 samples with 5 input values (MH0, MA0, MHC, lam2, lamL) and 8 target values (xsec_3535_13TeV, xsec_3636_13TeV, xsec_3737_13TeV, xsec_3537_13TeV, xsec_3637_13TeV, xsec_3735_13TeV, xsec_3736_13TeV, xsec_3536_13TeV) from a parameter space of the Inert Doublet Model chosen as: 50< MH0, MA0, MHC<3000GeV;−2π < lam2,lamL<2π. The cross sections were computed at leading order using MADGRAPH2.6.4 and the IDM UFO implementation from the FeynRules data base.</p> <p> </p> <p> </p>
Data for "Abrupt transitions in an atmospheric single-column model with weak temperature gradient approximation"
<p>These are the main output files for the experiment studied in the paper "Abrupt transitions in a single-column model with weak temperature gradient approximation". File names and descriptions are as follows:</p> <ol> <li>hysteresis.mat: A MATLAB file that contains WRF output from the hysteresis test used to create Figure 5 of the paper. The variable names should be straightforward to someone familiar with WRF. (Full WRF output was not saved for these runs.)</li> <li>input_soil: surface initial conditions for WRF SCM, same for all experiments</li> <li>input_sounding: initial sounding, same for all experiments</li> <li>main_fls1: a WTG SST-ramping experiment showing <span class="math-tex">\(f_{\rm LS}\to 1\)</span> as described in the paper.</li> <li>main_rce_ramp: a SST-ramping experiment without the weak temperature gradient approximation</li> <li>namelist.input.rce: the namelist input file for the "rce_run" experiment</li> <li>namelist.input.wtg: the namelist input files for the "wtg_ramp_run1" experiment</li> <li>rce_run: the main RCE experiment used to create the background WTG profile for the wtg_ramp_run1 experiment and the wtg_noramp experiment</li> <li>wtg_noramp: a 180-day experiment with WTG but no ramping, for comparison</li> <li>wtg_ramp_run1: the main WTG ramping experiment showing the abrupt transitions</li> </ol> <p> </p>
Data for Investigating the Technical Debt in Procedural Model Transformation Languages
<p>The content presents the data for Investigating the Technical Debt in Procedural Model Transformation Languages </p>
Data for manuscript: Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks
<p>Data used to calibrate and run the SeRFE model in the applications presented in the manuscript "Sediment Routing and Floodplain Exchange (SeRFE): A spatially explicit model of sediment balance and connectivity through river networks."</p>
Contextualisation of Data Flow Diagrams for security analysis: Pilot study model
<p>CAIRIS model package to accompany 'Contextualisation of Data Flow Diagrams for security analysis' GraMSec 2020 paper. </p> <p>The model package can be loaded in CAIRIS via the System >> Import Model menu.</p>
Data repository of model outputs in Ferrier and Perron (2020), "The importance of hillslope scale in responses of chemical erosion rate to changes in tectonics and climate"
<p>This is a repository of model outputs in Ferrier and Perron (2020), "The importance of hillslope scale in responses of chemical erosion rate to changes in tectonics and climate" at the Journal of Geophysical Research - Earth Surface. See the readme file for descriptions of the data contained in each file.</p>
Data for "Abrupt transitions in an atmospheric single-column model with weak temperature gradient approximation"
<p>These are the main output files for the experiment studied in the paper "Abrupt transitions in a single-column model with weak temperature gradient approximation". File names and descriptions are as follows:</p> <ol> <li>hysteresis.mat: A MATLAB file that contains WRF output from the hysteresis test used to create Figure 5 of the paper. The variable names should be straightforward to someone familiar with WRF. (Full WRF output was not saved for these runs.)</li> <li>input_soil: surface initial conditions for WRF SCM, same for all experiments</li> <li>input_sounding: initial sounding, same for all experiments</li> <li>main_fls1: a WTG SST-ramping experiment showing fLS→1 as described in the paper.</li> <li>main_rce_ramp: a SST-ramping experiment without the weak temperature gradient approximation</li> <li>namelist.input.rce: the namelist input file for the "rce_run" experiment</li> <li>namelist.input.wtg: the namelist input files for the "wtg_ramp_run1" experiment</li> <li>rce_run: the main RCE experiment used to create the background WTG profile for the wtg_ramp_run1 experiment and the wtg_noramp experiment</li> <li>wtg_noramp: a 180-day experiment with WTG but no ramping, for comparison</li> <li>wtg_ramp_run1: the main WTG ramping experiment showing the abrupt transitions</li> </ol>
Model code and data for "Mitigation of the double ITCZ syndrome in BCC-CSM2-MR through improving parameterizations of boundary-layer turbulence and shallow convection" by Lu et al., submitted to Geoscientific Model Development, https://doi.org/10.5194/gmd-2020-40, in review, 2020.
<p>Description of the files:</p> <p>“BCC_CSM2_MR.code.tar” contains the codes and run scripts for the medium-resolution Beijing Climate Center Climate System Model version 2 (BCC-CSM2-MR). Detailed description of the model refers to the paper “The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6” by Wu et al., Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019.</p> <p>“BCC_CSM2_MR.inputdata.tar” contains the input data needed to run the model.</p> <p>“REF_amip.rar” contains the output data from the REF_amip experiment.</p> <p>“NEW_amip.rar” contains the output data from the NEW_amip experiment.</p> <p>“REF_cmip.rar” contains the output data from the REF_cmip experiment.</p> <p>“NEW_cmip.rar” contains the output data from the NEW_cmip experiment.</p> <p>“UWMT_amip.rar” contains the output data from the UWMT_amip experiment.</p> <p>“mHack_amip.rar” contains the output data from the mHack_amip experiment.</p>
Data of the modelling results
<p>Data record of the numerical modeling results</p>
Data from: Modelling variability in the fire-response of an endangered bird to improve fire-management
Conservation managers regularly burn vegetation to regenerate habitat for fire-dependent species. When determining the time-since-fire at which to burn, managers model change in a species' occurrence over time, post-fire (fire-response curve) and identify the time-since-fire associated with decline in occurrence. However, where species exhibit variability in their fire-response across space, using a single fire-response curve to determine the timing of burns may lead to burning habitat at an inappropriate time-since-fire. We tested if elevation, local topography, soil properties, vegetation type or evapotranspiration affect the fire-response of the endangered mallee emu-wren Stipiturus mallee and its hummock-grass habitat Triodia scariosa in south-eastern Australia (n= 217). Previous work on the mallee emu-wren found a unimodal fire-response with decline in occurrence at ~30-50 years-since-fire and a time-window of occurrence of ~30 years. We found that time-since-fire and elevation interact to affect the mallee emu-wren fire-response. At high elevations (55-98 m), mallee emu-wrens declined in occurrence at ~50 years-since-fire, with a time-window of occurrence of 20-40 years. However, at low elevations (28-55 m), mallee emu-wrens showed no decline in occurrence with increasing time-since-fire with a time-window of occurrence of up to 107 years. Extent cover of Tall T. scariosa showed similar patterns to the mallee emu-wren, indicating that vegetation structure is a likely driver of variability in the mallee emu-wren fire-response. We speculate that the effect of low elevation is mediated by increased soil nutrient and water availability for key plants. We used our findings to map the appropriate time-since-fire at which to burn to regenerate habitat for the mallee emu-wren across the study-region. We recommend no burning for regeneration across one-third of potential habitat, because the mallee emu-wren showed no decline in occurrence in these areas. We recommend managers model variability in species' fire-responses across space to improve the timing of burns for regeneration.
Pretrained models and data for CC2Vec
<p>Data & Pretrained models for the experiments in the paper, CC2Vec: Distributed Representations of Code Changes.</p> <p> </p> <p>This research was supported by the Singapore National Research Foundation (Award number: NRF2016-NRFANR003) and the ANR ITrans project</p>
The Resilience of Habitable Climates Around Circumbinary Stars: 3D climate model data Part 1
<p>Climate modeling outputs used in the paper, "The Resilience of Habitable Climates Around Circumbinary Stars", to be published JGR-Planets Special Edition on Exoplanets. Files contain 4 Earth years of hourly time cadence outputs of basic climate fields. Hourly time-cadence is needed in order to grasp the temporal variations of circumbinaries. </p> <p> </p> <p> </p>
Raw Data of SLR for Parallelization, Modeling, and Performance Prediction in the Multi-/Many Core Area
<p>Contains the raw data and paper list of the SLR performed for the following publication:</p> <p>Parallelization, Modeling, and Performance Prediction in the Multi-/Many Core Area: A Systematic Literature Review</p>
Supplementary material for "Modeling Difficulties in Data Modeling: Similarities and Differences between Experienced and Non-experienced Modelers"
<p>This material supplements the following conference publication:</p> <p>Rosenthal, K., Strecker, S., Pastor, O.: Modeling Difficulties in Data Modeling: Similarities and Differences between Experienced and Non-experienced Modelers. In: ER 2020. Vienna, Austria (2020)</p>
Climate model data
<p>The experimental data are dynamics simulation files obtained from Open Science Data Cloud. The data files are in the common NetCDF (network Common Data Form) format (Springer, 2011). Each file is broken down into year and month, containing all the variables for one month. The data set includes air temperature data (T) stored as a(latitudelongitudeheight) tensor and 22 other attributes stored as a (latitudelongitudetime) tensor from 1980/01 through 1998/05.</p>
Aquaplanet experiment data for Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999
<p><strong>Aquaplanet experiment data from Webb and Lock (2020)</strong></p> <p><br> Webb, M. J., & Lock, A. P. (2020). Testing a physical hypothesis for the relationship between climate sen-sitivity and double-ITCZ bias in climate models.Journal of Advances in Modeling Earth Systems, 12,e2019MS001999.https://doi.org/10.1029/2019MS001999</p> <p>CSV files containing data from Figs 1(b) and 2(a-d)</p> <p>Figure 2b:</p> <p>APEQ.Precipitation_mmperday.zonal.csv<br> APEQ_2LW_Cloud.Precipitation_mmperday.zonal.csv<br> APEQ_3LW_Cloud.Precipitation_mmperday.zonal.csv</p> <p>Figure 3a:</p> <p>APEQ.w700.zonal.csv<br> APEQ_3LW_Cloud.w700.zonal.csv<br> APEQ_2LW_Cloud.w700.zonal.csv</p> <p>Figure 3b:</p> <p>APEQ.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_2LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv<br> APEQ_3LW_Cloud.Estimated_Inversion_Strength_K.zonal.csv</p> <p>Figure 3c:</p> <p>APEQ.Net_CRE_Wperm2.zonal.csv<br> APEQ_2LW_Cloud.Net_CRE_Wperm2.zonal.csv<br> APEQ_3LW_Cloud.Net_CRE_Wperm2.zonal.csv</p> <p>Figure 3d:</p> <p>APEQ4K-APEQ.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_2LW_Cloud-APEQ_2LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv<br> APEQ4K_3LW_Cloud-APEQ_3LW_Cloud.Net_CRE_Feedback_Wperm2perK.zonal.csv</p> <p>Any queries please contact Mark Webb mark.webb@metoffice.gov.uk</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.