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125 results for “open models”
Data and scripts for the publication "A case for open communication of bugs in climate models"
<p>Primary data and scripts for the publication "A case for open communication of bugs in climate models" (submitted to GMDD as EGUSPHERE-2024-3493)</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
The human splenic microcirculation is entirely open as shown by 3D models in virtual reality. Supplementary files
<p>These materials supplement our paper "The human splenic microcirculation is entirely open as shown by 3D models in virtual reality".</p> <p><strong>Summary</strong></p> <p>The human spleen is equipped with an organ-specific microcirculation. The initial part of the venous circulation is formed by spleen-specific large microvessels, the sinuses. Sinuses eventually fuse to form venules and veins. For more than 170 years there have been debates, whether splenic red pulp capillaries join sinuses, i.e., whether the microcirculation is closed or open - or even simultaneously closed and open. We have now solved this question by three-dimensional reconstruction of a limited number of immunostained serial sections of red and white pulp areas, which were visualized in virtual reality. Splenic capillaries have special end structures exhibiting multiple small diverging endothelial cell processes, which always keep a certain distance to the walls of sinuses. Only very few capillary ends were difficult to diagnose. Positive identification of these end structures permits to conclude that the human splenic microcirculation is entirely open. This is also true for the perifollicular capillary network and for capillaries close to red pulp venules. Follicles are supplied by a relatively dense open perifollicular capillary net, which is primarily, but not exclusively, fed by sheathed and few non- sheathed capillaries from the surrounding red pulp network.</p> <p> </p> <p><strong>Interactive models</strong></p> <p>Each <em>file_sX.zip</em> contains a 3D model, registered sequence of serial sections (the input data to generate and validate the model), and an interactive / VR viewer. After you have unzipped the file, there are multiple batch files. Any of them can be used for an interactive model display on a normal monitor, but we recommend the file <em>start_index.bat</em>. Now, if you have a virtual reality headset or a 4K monitor, there are better options:</p> <ul> <li>If you have a HTC Vive-compatible headset (i.e., the "wand" controllers, typically coming with HTC Vive, Vive Pro, Vive Pro 2, etc.), please use <em>start_vive.bat</em>.</li> <li>If you have a Valve Index headset (i.e., the "knuckles" controllers, should also work with Oculus devices), please use <em>start_index.bat</em>.</li> <li>If you have a high-resolution monitor (4K or better), please use <em>start_interactive_4k.bat</em> for a high-resolution interactive model.</li> </ul> <p>In the zip-file is also a <em>README.txt</em> file with instructions on the controls for the interactive and VR viewer. It is also available in the viewer at press of F1 button. In a nutshell: </p> <ul> <li>ASWD control the movement</li> <li>I (the "i" key) turns the sections on and off</li> <li>JK advance the sections</li> <li>CV adjust the height</li> <li>E turns the model on and off.</li> </ul> <p>The viewer executables are built for Windows. They work with Windows 10, should work with previous versions of Windows (64 bit) and also with future versions, such as Windows 11. Users of other OS, such as MacOS or Linux, can use <a href="https://www.meshlab.net/">MeshLab</a> to look at the model. Any image viewer can be used to inspect the sections in the <em>img</em> folder of the unpacked zip file. However, in this case, no VR experience and no simultaneous view of both 3D reconstruction and the sections is possible.</p> <p> </p> <p><strong>Videos</strong></p> <p>The videos with the same content are mostly supplied in three versions:</p> <ul> <li>On any modern hardware you should be able to play the H.265 videos, ending in <em>...4K_h265_10bit.mov</em>, there are in 4K resolution</li> <li>If it is not the case, but you want 4K resolution, use the H.264 version, ending in <em>...4K_h264_10bit.mov,</em> it is also in 10 bit quality</li> <li>A fallback for weaker hardware and maximal compatibility is the H.264 FullHD version. It should play anywhere. Those files end in <em>...1080p_h264.mov.</em></li> </ul> <p> </p> <p><strong>Supplementary Figures S1 and S2</strong></p> <p>The supplementary figures with their legends are available in the file <a href="https://zenodo.org/record/6599487/files/supp.pdf"><em>supp.pdf</em></a>.</p> <p> </p> <p><strong>3D models corresponding to Figs. 4a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s1.zip?download=1">Supplementary file S1</a>. 3D model of ROI 1 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s2.zip?download=1">Supplementary file S2</a>. 3D model of ROI 2 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s3.zip?download=1">Supplementary file S3</a>. 3D model of ROI 3 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s4.zip?download=1">Supplementary file S4</a>. 3D model of ROI 4 with open capillary ends in red</p> <p> </p> <p><strong>File corresponding to Fig. 7a</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s5.zip?download=1">Supplementary file S5</a>. 3D model of sinus network and open capillary ends in red</p> <p> </p> <p><strong>Files corresponding to Figs. 9a,b</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s6.zip?download=1">Supplementary file S6</a>. 3D model of ROI 2 with perifollicular capillary network in red correspondig to Fig. 9a</p> <p><a href="https://zenodo.org/record/6599487/files/file_s7.zip?download=1">Supplementary file S7</a>. 3D model of ROI 2 with perifollicular capillary network in red and open ends in yellow corresponding to Fig. 9b</p> <p> </p> <p><strong>Videos corresponding to Figs 5a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s1_4K_h264_10bit.mov">Supplementary video S1</a>. Two capillaries with open ends in Fig. 5a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s2_4K_h264_10bit.mov">Supplementary video S2</a>. Capillary with at least two open ends in Fig. 5d-f</p> <p> </p> <p><strong>Videos corresponding to Figs 6a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s3_1080p_h264.mov">Supplementary video S3</a>. Quality control of open ends shown in Fig. 5a-c and Fig. 6a,b</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s4_1080p_h264.mov">Supplementary video S4</a>. Quality control of open end shown in Fig 5d-f and Fig. 6c,d</p> <p> </p> <p><strong>Videos corresponding to Fig. 4d and Figs 8a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s5_1080p_h264.mov">Supplementary video S5</a>. Control of open capillary end shown in Fig. 8a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s6_1080p_h264.mov">Supplementary video S6</a>. Control of open capillary end shown in Fig. 8d-f</p>
Bayesian Samples and Data Behind Figures: Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I
<p>Auxiliary data associated with the article <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.6145P/abstract">"Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I: M 35, NGC 6819, NGC 188" by Penev, K & Schussler, J</a></p> <p>The type of data corresponds to a particular filename format. Bayesian samples are in HDF5 format, directly as saved by the <a href="https://emcee.readthedocs.io/en/stable/index.html">emcee</a> sampler (see <a href="https://emcee.readthedocs.io/en/stable/user/backends/">https://emcee.readthedocs.io/en/stable/user/backends/</a>). All other files are in AAS-journal style machine readable tables format generated by <a href="https://github.com/cds-astro/cds.pyreadme">cdspyreadme</a> python library.</p> <p>Description of contents by filename format:</p> <pre><code><CLUSTER>_<BINARY ID>_.*.h5</code></pre> <p>Bayesian analysis samples constraining the tidal dissipation efficiency of the given binary. The values of the sampled system and tidal dissipation parameters are stored as blobs (<a href="https://emcee.readthedocs.io/en/stable/user/blobs/">https://emcee.readthedocs.io/en/stable/user/blobs/)</a></p> <pre><code><CLUSTER>_<BINARY ID>_lgQ_period.mrt</code></pre> <p>The 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary as a function of tidal period</p> <pre><code><CLUSTER>_<BINARY ID>_burnin_period.mrt</code></pre> <p>The MCMC burn-in period before the 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary are considered converged (see article text).</p> <pre><code><CLUSTER>_<BINARY ID>_cdfstd_period.mrt</code></pre> <p>The standard deviation of the <span class="math-tex">\(CDF(\log_{10}Q_\star')\)</span> for the given binary as a function of tidal period for each of the quantiles. The maximum likelihood value is the target percentile, i.e. one of: 2.3%, 15.9%, 84.1%, and 97.7%</p>
Making the Most out of a Hydrological Model Dataset: Sensitivity Analyses to Open the Model Black-Box (data and code)
<p>This is "data and code" repository for the Water Resources Research Article 2017WR020401 by Borgonovo et al. (2017): "Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box". Each sub-directory contains the Matlab or R scripts to reproduce all paper plots. </p> <p>Note, that the data of this repository (i.e. under ./data_input ) are identical to the data analysed by Rakovec et al. (2014).</p> <p>References:</p> <ul> <li>Borgonovo, E., Lu, X., Plischke, E., Rakovec, O. and Hill, M. C. (2017), Making the most out of a hydrological model data set: Sensitivity analyses to open the model black-box. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2017WR020767</li> <li>Rakovec, O., M. C. Hill, M. P. Clark, A. H. Weerts, A. J. Teuling, and R. Uijlenhoet (2014), Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, doi:10.1002/2013WR014063.</li> </ul>
Open access data for 2-year percutaneous osseointegrated implants - A sheep model
<p>Percutaneous osseointegrated <strong>(OI)</strong> devices for amputees are metallic endoprostheses, surgically implanted into the residual bone that protrude through the skin, allowing attachment of an exoprosthetic. In contrast to standard socket-type systems, these percutaneous OI devices can provide an improved prosthetics attachment platform. However, bone adaptations, which include atrophy and/or hypertrophy along the extent of the host bone-endoprosthetic interface, are known clinical outcomes and are dependent upon the load transfer region of the device to the host bone. The goal of this study was to determine if a percutaneous OI device, designed with a porous coated distal region and a collar, could promote and maintain stable bone attachment. A total of eight, 18 to 24-month old, mixed-breed sheep were surgically implanted with a percutaneous OI device. For 24-months, animals were allowed to bear weight as tolerated and monitored for signs of bone remodelling. At necropsy, the endoprosthesis and the surrounding tissues were harvested, radiographically imaged, and histomorphometrically analyzed to determine the periprosthetic bone adaptation in five animals. Bone growth into the porous coating was achieved in all five animals. Serial radiographic data showed stress-shielding related bone adaptation based on the placement of the endoprosthetic stem. When collar placement achieved end-bearing against the transected bone, distal bone conservation/hypertrophy was observed. The results supported the use of distally porous coated percutaneous OI devices for distal load-transfer and host bone maintenance.</p>
The adoption of Article Processing Charges as a business model by Brazilian Open Access journals
<p>These are the raw data behind the publication:</p> <p>Appel, A.L.; Albagli, S. The adoption of Article Processing Charges as a business model by Brazilian Open Access journals. <em>Transinformação</em>, 31:e180045, 2019. <a href="https://doi.org/10.1590/2318-0889201931e180045">https://doi.org/10.1590/2318-0889201931e180045</a></p> <p>Different funding and business model alternatives for Open Access to scientific publication have been discussed and tried, either by Gold open access journals or by the ‘hybrid’ ones. A growing number of both types of scholarly journals have adopted a publication fee – more specifically an Article Processing Charge – as their open access business model, a procedure that has been the subject of controversies. The objective of this study is to characterize Brazilian open access journals that adopt article processing charges. The main contribution of this study is to audit and support decision making of editorial policies and business models for open access that are being proposed by and for Brazilian journals. We defined a sample of Brazilian open access journals using article processing charges extracted from the Directory of Open Access Journals database in April 2018 along with their classification in the Scimago Journal Ranking, Journal Citation Reports and Qualis Capes system, considered as an indicator of prestige of academic journals. The study reveals that a small number of Brazilian open access journals are currently applying article processing charges, with practices varying mainly according to fields of study, types of organization and classification according to the Qualis system.</p>
Tutorial Weather Data Cutouts for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>The provided lightweight <strong>cutouts </strong>are spatiotemporal subsets of the German weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset for March 2013 to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Model run with METROMS to evaluate open boundary conditions in CICE [idealized wind]
<p>Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>.</p> <p>The model was initialized from the TOPAZ4 model (Sakov et al., 2012)<strong> </strong>and was run for the period 2019.09.01 - 2019.09.20, one time without OBC and one time with OBC enabled using input data from TOPAZ4<strong> </strong>at the boundaries. In both runs the model was set up realistically, but with the exception of idealized wind forcing. More specifically, the wind was blowing 10 m/s in the positive xi-direction until 2019-09.07 and then 10/m/s in the negative xi-direction for the rest of the simulation. The reasoning for this was to clearly demonstrate the effects of the OBC. Initially, the wind is blowing the ice away from the boundary and without using OBC for ice, it leaves open water in its path due to no information coming in through the boundary. With OBC enabled however, the simulation appear a lot more sensible with sea ice from TOPAZ4 coming in through the boundaries. When the wind switches to the opposite direction after 2019.09.07, on the case without OBC results in ice piling up at the boundary after some time. However, with OBC enabled, the ice exists the model domain.</p> <p> </p>
Model run with METROMS to evaluate open boundary conditions in CICE
<p>Support for time-varying open boundary conditions (OBC) have been developed for sea ice in the Los Alamos Sea Ice Model (CICE) by Pedro Duarte (NPI, Norway). This dataset is a result of using the coupled ocean (ROMS) and sea ice (CICE) modelling framework METROMS (<a href="https://github.com/metno/metroms">https://github.com/metno/metroms</a>) in order to test the effects of the above mentioned boundary conditions. The specific application of METROMS that was used was MET Norway's main forecasting system for the Barents Sea; the Barents-2.5km model; details about the model can be found at <a href="https://ocean.met.no/models">https://ocean.met.no/models</a>.</p> <p>The model was initialized from the TOPAZ4 model (Sakov et al., 2012)<strong> </strong>and was run for the period 2019.09.01 - 2019.10.03, one time without OBC and one time with OBC enabled using input data from TOPAZ4<strong> </strong>at the boundaries. This dataset can further be used to compare with satelite observations of sea ice concentration in order to help evaluate the impacts of the OBC's in CICE.</p>
Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness
Text-fig. 5. Macroevolutionary trends related to the IC model in the first three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dashed line (- -) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).
Text-fig. 2. Developmental morphospace of molariform ratios in Lestodon armatus compared to the IC model. Dash-dot line (-.-), show OLS line; dash double-dot line (-..-) shows RMA line. in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness
Text-fig. 2. Developmental morphospace of molariform ratios in Lestodon armatus compared to the IC model. Dash-dot line (-.-), show OLS line; dash double-dot line (-..-) shows RMA line.
Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle). in Unexpected Inhibitory Cascade In The Molariforms Of Sloths (Folivora, Xenarthra): A Case Study In Xenarthrans Honouring Gerhard Storch'S Open-Mindedness
Text-fig. 4. Macroevolutionary trends related to the IC model in the last three teeth of the six families of extinct sloths, as well as specimens of the "basal Megatherioidea", Pseudoglyptodon, and Bradypus. Dash-dot line (-.-) shows the regression including all data; solid line shows the regression after the exclusion of Octodontotherium (shown in the plot as a filled triangle).
Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
<p>The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System.</p> <p>Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication.</p> <p><br> The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020.</p> <p>The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework.</p> <p>We encourage you to contribute to improving the datasets.</p>
PyPSA-Eur: An Open Optimisation Model of the European Transmission System (Dataset)
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. The software pipeline to assemble the model is developed at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a> and documentation is available at <a href="http://pypsa-eur.readthedocs.io">pypsa-eur.readthedocs.io.</a></p> <p><strong>This repository provides pre-built PyPSA networks resulting from corresponding PyPSA-Eur Releases using the default configuration!</strong></p> <p>The model alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p>It only includes freely available and open data. It provides a fully automated free software pipeline to assemble the load-flow-ready model from the original datasets, which enables easy configuration, replacement and<br> improvement of the individual parts.</p> <p>The model is suitable both for operational studies and generation and transmission expansion planning studies.</p> <p>Some basic validation is provided in a paper describing the dataset:</p> <ul> <li>Jonas Hörsch, Fabian Hofmann, David Schlachtberger, and Tom Brown. PyPSA-Eur: An open optimisation model of the European transmission system. Energy Strategy Reviews, 22:207-215, 2018. <a href="https://arxiv.org/abs/1806.01613">https://arxiv.org/abs/1806.01613</a>, <a href="http://https://doi.org/10.1016/j.esr.2018.08.012">https://doi.org/10.1016/j.esr.2018.08.012</a>.</li> </ul>
Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)
<p>Output raster datasets from the 2023 Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p>Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor. </p> <p>NOTE- These outputs were created using tools stored in this repository: <a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a> as well as several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220. </p> <p> </p> <p> </p> <p> </p>
Input raster datasets for Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)
<p>Input raster datasets used to create an Ecological Condition Model (ECM) for open pine ecosystems in the Apalachicola Regional Restoration Initiative area of the eastern Florida Panhandle. Our goal was to develop an ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p> </p> <p> </p>
Data from: An open spatial capture–recapture model for estimating density, movement, and population dynamics from line-transect surveys
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Monitoring animal populations with cameras using open, multistate, N-mixture models
Open the record for dataset details and reuse information.
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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.