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openbsd-3-clauseDec 2023View details →

Southern California Earthquake Center (SCEC) Community Geodetic Model (CGM)

OverviewMeasuring accurately the relative movement of the surface of the Earth is a critical constraint on the slow and broad tectonic loading and unloading to which faults respond, and is one of the few observations of the solid Earth that may be made directly without inference. High-precision geodetic observations, such as from Global Navigation Satellite Systems (GNSS), which includes the Global Positioning System (GPS), and interferometric synthetic aperture radar (InSAR), allow measurement of fault motions between, during and in the aftermath of earthquakes and other related tectonic phenomena, densely in both space and time.The Community Geodetic Model (CGM) provides velocities and time series of observed points on the Earth's surface over Southern California using data from a number of contributing researchers, institutions and analysis centers. The GNSS products provide high temporal resolution (nominally daily measurement points for continuous stations) in three dimensions at specific observation sites and the InSAR products provide high spatial resolution (approximately one point per tens of m on the ground, depending on exact specifications of data and processing). Combined, they provide the ability to study crustal deformation over a wide range of distances and periods.The CGM differs from other<a href="https://www.scec.org/research/cxm"> SCEC Community Models</a> in that it is constantly extending with time as new data are acquired daily, so it is not static.The CGM version 1 (2016; <a href="https://doi.org/10.5281/zenodo.4926528">doi:10.5281/zenodo.4926528</a>) was a collection of time-independent (velocity-only) geodetic products gathered from published papers. The GNSS velocities were then combined and modeled by a Working Group researching methods and contributing interpolated deformation fields. The main goal of the CGM version 2 is to add time-dependent (time series) products to both the GNSS and InSAR products. For the GNSS, this is done by ingesting survey and (mostly) continuous time series from five analysis centers in the U.S.: the Geodetic Facility for the Advancement of Geoscience (GAGE); the Nevada Geodetic Laboratory (NGL) at the University of Nevada, Reno (UNR); the NASA Jet Propulsion Laboratory (JPL) and Scripps Orbital and Permanent Array Center (SOPAC) contributions to the MEaSUREs ESESES project; and the U.S. Geological Survey (USGS). Like the various contributions to the CGMv1 GNSS velocities, these time series are rigorously adjusted to be self-consistent, before a weighted mean is calculated to produce the consensus products. Much of the InSAR contribution is a consensus from research by the SCEC community within the CGM (InSAR) Working Group, whose individual contributions are listed below and in more detail in the README.txt file in the top directory of the archive. The CGMv2 is therefore a "union" or "superset" of survey and continuous GNSS and InSAR time series.Please see<a href="https://www.scec.org/research/cgm"> https://www.scec.org/research/cgm</a> for more information.Version: CGMv2.0.0This is the second major release of the CGM (version 2.0.0) and is distributed as a zip-file. See below and in the README.txt file for information about the directory structure and contents of the entire zipped archive. Much of the SCEC5 activity was focused on the assembly of GNSS and InSAR time series for measuring temporally variable motions, expanding the CGMv1 with the time dimension. The CGMv2.0.0 is a time-dependent set of products, consisting of time series and velocities of the Earth's surface measured by GNSS and InSAR.Directory Structure and Contentsdata/gnss/pos/ The CGMv2.0.0 GNSS time series in <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">"pos" format</a> (plain text), relative to various reference frames described below. Header lines in each file provide information about the nominal reference coordinates and data columns. Files named "*.wmrss_*" are the continuous stations (weighted mean with rescaled sigma) and files named "*.final_" are the survey sites.data/gnss/pos/igb14/ The International GNSS Service's (IGS's) <a href="https://lists.igs.org/pipermail/igsmail/2020/007917.html">revised realization of ITRF2014</a>data/gnss/pos/nam14/ North America defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion modeldata/gnss/pos/pcf14/ The Pacific defined by <a href="https://doi.org/10.1093/gji/ggx136">Altamimi et al.'s (2017)</a> ITRF2014 plate motion modeldata/gnss/pos/nam17/ North America defined by <a href="https://doi.org/10.1029/2017JB015257">Kreemer et al. (2018)</a>data/gnss/vel/ The CGMv2.0.0 GNSS velocities in a CSV file similar to <a href="https://www.unavco.org/data/gps-gnss/derived-products/docs/NOTICE-TO-DATA-PRODUCT-USERS-GPS-2013-03-15.pdf">GAGE's "vel" format</a> (plain text), relative to the same reference frames described above. Header lines in each file provide information about the data columns.data/insar/ The CGMv2.0.0 InSAR line-of-sight consensus time series and velocities for four ESA Sentinel-1 tracks (ascending tracks 64 and 166, and descending tracks 71 and 173) over Southern California, in an <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers#cgm-insar-hdf5-structure">HDF5 format designed for the CGM</a>. A description of and reader for the HDF5 files may be found <a href="https://github.com/kmaterna/InSAR_CGM_readers_writers">here</a>.data/insar/contrib/ Individual contributions to the InSAR time series and velocity products, as described below and in more detail in the top-level README.txt file.ContributorsThe GNSS time series are a weighted mean, after restoration of global scale if processed using Gipsy (JPL, NGL/UNR and USGS) and self-consistent alignment of reference frame, of the following GNSS analysis centers, whose products are publicly available at the embedded hyperlinks:<ul><li>The <a href="https://www.unavco.org/data/gps-gnss/derived-products/derived-products.html">Geodetic Facility for the Advancement of Geoscience (GAGE)</a> (<a href="https://doi.org/10.1002/2016RG000529">Herring et al., 2016</a>)</li><li>The <a href="http://geodesy.unr.edu/">Nevada Geodetic Laboratory</a> at the University of Nevada, Reno (<a href="https://doi.org/10.1029/2018EO104623">Blewitt et al., 2018</a>)</li><li>NASA's <a href="http://garner.ucsd.edu/pub/solutions/gipsy">Jet Propulsion Laboratory contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li><a href="http://sopac-csrc.ucsd.edu/">SOPAC</a>'s <a href="http://garner.ucsd.edu/pub/measuresESESES_products/Timeseries/">contribution</a> to the <a href="http://sopac-csrc.ucsd.edu/index.php/measures-2/">MEaSUREs ESESES Project</a></li><li>The <a href="https://earthquake.usgs.gov/monitoring/gps">United States Geological Survey</a> (<a href="https://doi.org/10.1785/0220160204">Murray and Svarc, 2017</a>)</li><li><a href="https://www.scec.org/user/zshen">Zheng-Kang Shen's (UCLA)</a> <a href="http://scec.ess.ucla.edu/~zshen/cgm/">survey time series</a></li></ul>Z.-K. Shen processed the raw data from the <a href="https://service.scedc.caltech.edu/gps/">SCEC survey-mode GPS data archive</a> to provide the corresponding time series and velocities. A. Gonzalez Ortega provided processed time series from <a href="https://regnom.cicese.mx/">CICESE's REGNOM network of continuous GNSS stations</a>. M. Floyd and T. Herring designed the download, alignment and combination of the publicly available continuous GNSS archives, listed above, in various reference frames.Contributions from individuals and institutions within the SCEC community to the CGM (InSAR) products are:<ul><li>K. Wang contributed time series and velocity solutions</li><li>K. Guns and X. Xu contributed time series and velocity solutions</li><li>Z. Liu contributed time series and velocity solutions</li><li>S. Sangha, M. Govorcin and D. Bekaert contributed time series and velocity solutions</li><li>G. Funning contributed time series and velocity solutions</li><li>E. Tymofyeyeva calculated the combination of contributed solutions to generate the consensus product</li><li>K. Materna contributed time series and velocity solutions, and wrote the translation tools for converting to and from HDF5 format, as designed by all InSAR contributors listed immediately above plus M. Floyd</li></ul>Three groups (K. Guns and X. Xu; Z. Liu; and S. Sangha, M. Govorcin and D. Bekaert) independently processed interferograms from common raw datasets using different processing approaches.E. Tymofyeyeva coordinated and led the InSAR Working Group.M. Floyd coordinated and led the wider CGM Working Group.All contributed to the design of the HDF5 format in which the InSAR products are distributed.

opencc-by-4.0Jan 2024View details →

Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions

This dataset was used in the case study of the following publication:  - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a>  (if you used this dataset in your publications, please send us your information so we can add your publication to the list above)   The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days. The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).   We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.

openbsd-3-clauseJul 2023View details →

SCEC Community Stress Model (CSM)

<a href="https://www.scec.org/science/csm" target="_blank" rel="noopener">The SCEC Community Stress Model (CSM)</a> is a group of models of stress and stressing rate within the California crust and lithosphere, compiled by the CSM Working Group of the <a href="https://www.scec.org">Statewide California Earthquake Center (SCEC)</a>. “Stress” models are models of the 3-D symmetric stress tensor (6 components), describing the forces present within a continuous volume. “Stressing rate” models are models of how the 3-D stress tensor changes over time, for example due to stress accumulating along a tectonic plate boundary. The CSM is one of several <a href="https://www.scec.org/science/cem">SCEC Community Earth Models</a>, but unlike some, it does not attempt to provide a single consensus model of stress or stressing rate. Instead, the intention is to compile a set of models of stress and stressing rate. The current version of the CSM includes 22 distinct models: 14 of stress, and 8 of stressing rate. The model techniques, assumptions, and input datasets vary, so no two models provide the exact same result. In some cases, the contributed models have been published in a peer reviewed journal. In other cases, the methods or datasets were previously published in a peer reviewed journal, but the exact contributed model is not published. For the fourteen stress models, the orientation of the stress tensor is mostly derived from the inversion of earthquake focal mechanisms. Eleven of the models are based solely on earthquake focal mechanisms, and thus do not provide information on stress magnitude. One model provides an estimate of the deviatoric stress required to support existing topography. Two models provide an estimate of the full absolute stress tensor, based on forward physics-based modeling of the tectonic loading of the southern California fault system. Note that even when sampled “on” a major fault surface, these model values should be regarded as estimates of stress in the adjacent continuum rather than estimates from “within” fault gouge zones. The eight stressing rate models are based either on geodetic data or on kinematic or mechanical models constrained by such data. Some are based on estimates of velocity field or strain rate that are then translated into stressing rate. Others involve forward physics-based modeling of tectonic loading of the fault system, but focus on deriving stressing rate rather than the absolute stress tensor. Each of the models was contributed by an author or group of authors, the earliest in 2012, the most recent in 2024, and some models were taken directly from the literature. Values in the crust (≤ 25 km depth) are reported for a uniform grid of latitude and longitude points, covering all of California at a resolution of ~2 km (0.0180º latitude, 0.0217º longitude), for a total of 160,663 unique points. Values in the deeper lithosphere (≥ 50 km) are reported for a uniform grid with a resolution of ~5 km (0.0450º latitude, 0.0543º longitude), for a total of 25,666 unique points. Depths are reported every 2 km from 1 km to 25 km depth, and then every 25 km to 100 km depth, for a total of 16 unique depths. Points that lie outside the defined volume for a particular model, given its assumptions and available data, are omitted from that model’s file. The model contributions include the 6-component cartesian stress tensor (in East, North, Up, tension positive) at each grid point, and then several values calculated from the tensor components that describe different aspects of the orientation or magnitude of the 3-D tensor. For visualization of these models, see the CSM web explorer on the SCEC CSM homepage, <a href="https://www.scec.org/science/csm">https://www.scec.org/science/csm</a>. Between 2012 and 2016, the CSM working group undertook a series of comparison exercises to assess consistency among southern California stress and stressing rate models that were available at that time. Broadly, the stress models tended to agree on stress axis orientations but not on magnitudes. Stressing rate models tend to agree along major fault segments, but disagree in secondary fault or off-fault areas. For details see the archived homepage for SCEC CSM v2023, <a href="https://southern.scec.org/research/csm">https://southern.scec.org/research/csm</a>. KEY TECHNICAL DEVELOPMENTS In 2024, as SCEC transitioned to a focus on California statewide, we compiled existing estimates of stress orientation from published literature for regions outside the original Southern California focus area. We also developed new statewide stressing rate models, based on recent estimates of strain rate across the western United States. These resulted in 11 additional contributions to the CSM.

opencc-by-4.0Dec 2021View details →

Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"

Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019

dryad40/100

Dataset for greenhouse gas modelling in diesel dependent communities transitioning to bioenergy

The data presented here are from the research article entitled "Greenhouse gas mitigation potential of replacing diesel fuel with wood-based bioenergy in an arctic Indigenous community: A pilot study in Fort McPherson, Canada". Based on a pilot study realized in Northern Canada and life cycle assessment, we provide a set of key parameters and operational data gathered along the biomass supply chain to build a GHG mitigation scenario and compute the quantity and timing of GHG savings in the off-grid community of Fort McPherson, NWT. Given that GHG mitigation scenarios are often assessed against a relative fossil-fuel reference scenario, we are providing two categories of data; 1) data for the reference fossil fuel scenario and; 2) data along the upstream operations of biomass supply chains. Both categories contain data related to the operational processes as well as forest growth or decomposition of unused feedstock. Although the data presented are mostly derived from the boreal forest, they could help guide other communities beyond the boreal to develop a renewable bioenergy system and assess their GHG mitigation options.

opencc-zeroJun 2022View details →

opencc-by-4.0Sep 2022View details →

3D models: the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)

This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB)  and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative. Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.

opencc-by-4.0Oct 2022View details →

PICASO 3.0 Atmospheric Models of WASP-39 b for the JWST Transiting Exoplanet Community Early Release Science Program

OVERVIEW The exoplanetary atmospheric models used in the recent <a href="https://www.nature.com/articles/s41586-022-05269-w">discovery of CO2  in WASP- 39 b's atmosphere</a> by the JWST transiting exoplanet community early release science program are presented here. These models are also being used to analyze multiple observations of WASP 39-b obtained using various JWST instruments and observational modes by the transiting exoplanet ERS team. The 1D Radiative-Convective-Thermochemical Equilibrium (RCTE) atmospheric models were computed using the open-source 1D climate model <a href="https://natashabatalha.github.io/picaso/">PICASO 3.0</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>). These atmospheric models were then post-processed with condensation clouds using the open-source cloud model <a href="https://natashabatalha.github.io/virga/">VIRGA</a> (<a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>). The atmospheric models were also post-processed with the 1D photochemical network code <a href="https://github.com/exoclime/VULCAN">VULCAN</a> (<a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a>) to explore photochemistry in WASP-39 b's atmosphere. 1D RCTE CLOUD-FREE MODELS The base 1D RCTE grid includes atmospheric metallicity points at 0.1, 0.3, 1.0, 3.0, 10.0, 30.0, 50.0, and 100.0x solar values. The Carbon-to-Oxygen (C/O) ratio value is varied between four values - 0.23, 0.46, 0.69, and 0.92. The intrinsic temperature of the planet has been varied across 100, 200, and 300 K, whereas two values of the heat redistribution factor - 0.4 and 0.5 are included. A heat redistribution factor of 0.5 corresponds to the case of full heat redistribution. With these grid points, the grid includes a total of 8x4x3x2= 192 different models. These models are in the "RCTE_cloud_free.zip" folder. The naming scheme of these files is "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_.nc" where [T_int] represents the intrinsic temperature of the planet, [MH] is the log10 of the atmospheric metallicity relative to solar, [CtoO] is the C/O ratio relative to solar, and [rfacv] is the heat-redistribution factor. So, a metallicity value of 0.3xsolar will have a [MH] value of -0.5, and a C/O 0.46 is considered 1xsolar and will correspond to [CtoO]=1. [T_int] and [rfacv] can assume values described in the previous paragraph. 1D RCTE CLOUDY MODELS The base 1D RCTE cloud-free models were post-processed to include condensation cloud species Na2S, MnS, and MgSiO3. The cloud structure and optical property calculations were performed using the VIRGA model where the sedimentation efficiency fsed and the vertical eddy diffusion coefficient (Kzz) are free parameters. For the cloudy models, 5  fsed  values - 0.6, 1, 3, 6, and 10 were used along with 3 different values of log10Kzz - 5, 7, 9, and 11, where Kzz is in cm2/s. These models are included in the "RCTE_cloudy.zip" folder following the naming structure "profile_eq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc" where two other variables are added in the name - [log10Kzz] and [fsed]. Both of these variables can take values listed here. PHOTOCHEMICAL CLOUD-FREE MODELS A much smaller subset of the base 1D RCTE models were post-processed with the 1D photochemical network code VULCAN to simulate the effects of vertical mixing and photochemistry in WASP-39 b's atmosphere. log10Kzz  was varied again between the 5, 7, 9, and 11 for this purpose. These files are named as "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz].nc" and can be found in the "photochem_cloud_free.zip" folder. PHOTOCHEMICAL CLOUDY MODELS The photochemical models were post-processed with clouds to simulate a cloudy atmosphere with disequilibrium chemistry. The fsed  and log10Kzz  grid system for the RCTE cloudy models has been used again for these models as well. These files are in the "photochem_cloudy.zip" folder and are named according to the format "profile_diseq_planet_[T_int]_grav_4.5_mh_[MH]_CO_[CtoO]_sm_0.0486_v_[rfacv]_kzz_1e[log10Kzz]_fsed_[fsed].cld.nc". FILE FORMATTING AND USAGE All the files are released in the <a href="https://docs.xarray.dev/en/stable/">xarray</a> format. Each model has one single xarray file containing all metadata of that model. This metadata includes the input parameters used to compute the model, for example, the metallicity, C/O ratio, and intrinsic temperature. The temperature-pressure (T(P)) profile and the volume mixing ratio profiles of all the different gases in each model is also included in the metadata. The computed transmission spectrum of the model planet from 0.3-6 microns is included in the same file as well. The spectrum is calculated with resampled opacities at a spectral resolution of 60,000, but they should be re-binned at a spectral resolution of 3000 or less for comparison with observed data. For cloudy models, the wavelength dependant optical depth, asymmetry parameter, and single scattering albedo for each atmospheric layer are included in these xarray files. We refer to this <a href="https://natashabatalha.github.io/picaso/notebooks/codehelp/data_uniformity_tutorial.html#Reading/interpreting-an-xarray-file">PICASO tutorial</a> for reading/writing these xarray files. The spectrum from these xarray files can be easily extracted using the following code. <pre><code class="language-python">import xarray as xr path = "path/to/files" ds_sm = xr.open_dataset(path+"profile_eq_planet_300_grav_4.5_mh_+2.0_CO_2.0_sm_0.0486_v_0.5_.nc") # for spectrum wavelength = ds_sm['wavelength'].values transit_depth = ds_sm['transit_depth'].values # for T(P) profile temperature = ds_sm['temperature'].values pressure = ds_sm['pressure'].values</code></pre> <a href="https://github.com/natashabatalha/picaso/blob/master/docs/notebooks/fitdata/GridSearch.ipynb">This tutorial</a> shows how to use these models to analyze the NIRSpec Prism observations of WASP-39 b, which led to <a href="http://www.nature.com/articles/s41586-022-05269-w">CO2 detection</a>. Please note that the folders must be unzipped before using them with this notebook. CREDITS If you use these modeling products in your work, please cite this zenodo repository along with the following papers depending on the part of the grid being used: 1) RCTE_cloud_free.zip  <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  2) RCTE_cloudy.zip <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  3) photochem_cloud_free.zip  <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>  4) photochem_cloudy.zip  <a href="https://arxiv.org/abs/2108.01790">Tsai et al. (2021)</a> , <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...925...33R/abstract">Rooney et al. (2022)</a>

Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).

Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).

Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).

Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.

Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel

Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).

opencc-by-4.0Sep 2019View details →

Fig. 16. Maximum Composite Likelihood model for 29 in Species turnover between the northern and southern part of the South China Sea in the Elaphropeza Macquart mangrove fly communities of Hong Kong and Singapore (Insecta: Diptera: Hybotidae)

Fig. 16. Maximum Composite Likelihood model for 29 species of Elaphropeza based on COI barcodes from specimens from Singapore and Hong Kong.

Fig. 7 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 7. The effect of fertilization, liming and soil acidification of the soil of the old coppices on Carabidae communities. Explanation of abbreviations please see in the text.

Fig. 4 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 4. The effect of unevenaged forest stands fires on their Carabidae communities in Ostrow Mazowiecka

Fig. 5 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 5. The effect of unevenaged forest stands fires on their Carabidae communities in Solec Kujawski

Fig. 3 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 3. The SCP/MIB model presenting the development of the Carabidae communities as taking part in the course of the Scots pine forest stands production cycle, in the post arable ground (dotted line) and in the forest soil (solid line). Denotations I. Post arable grounds: A – arable ground, A1 - 2-6- year old forest cultures, A2 - 8-14 year old coppices, Pdc3 - 18-year old coppices, A4 - 25-year-old forest stands, A5 - 33-62-year old forest stands, A6 – old growth (98-year old) forest stands. II. Forest grounds: F1 - 2-3- year old forest cultures, F2 - 6-11- year old coppices, F3 - 18 22-year old forest stands, F4 - 25-42- year old forest stands, F5 - 62-98 year old forest stands.

opencc-by-4.0Jan 2023View details →

Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"

This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms". The directory contains: trained_model_1_weights.zip: Trained model weights for first place solution (Team 1). trained_model_2_weights.zip: Trained model weights for second place solution (Team 2). trained_model_3_weights.zip: Trained model weights for third place solution (Team 3). trained_model_weights_baseline.zip: Trained model weights for the baseline model.

opencc-by-4.0Mar 2023View details →

Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Files used in initial submission of manuscript to Geoscientific Model Development. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.