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2,744 results for “Restoration”

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zenodo40/100

Data repository - The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

This datasets provides regional and spatial-explicit gridded data for the analysis presented in the manuscrip "The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios" under review in "Environmental Research: Climate" with reference "ERCL-100126"

opencc-by-4.0Feb 2023View details →

dryad40/100

Where should they come from? Where should they go? Several measures of seed source locality fail to predict plant establishment in early prairie restorations

<ol> <li>During the "decade on restoration," we must understand how to reliably re-establish native plant populations. When establishing populations through seed addition, practitioners prioritize obtaining seed from locations geographically near the restoration site (i.e., "local seed sourcing"). They are assumed to be under similar environmental conditions to the restoration site and should establish more robust plant populations and preserve local biotic interactions than seeds sourced from further away. However, this assumption remains virtually untested in realistic restoration settings and the importance of seed sourcing, relative to other factors such as seeding rate and management regimes, is unclear.</li> <li>To determine if seed source impacts plant establishment, abundance, and phenology, we developed a partnership between university researchers and a native seed producer that kept records on where their seed was sourced from and where it was planted. At each site, we recorded the abundance and phenological stage of five commonly used tallgrass prairie restoration species seeded at 24 sites undergoing restoration across Michigan. We considered two measures of seed source locality: geographic distance (seeds were sourced from locations 6–750km away from their respective restoration sites) and climate distance. We also obtained data on the seeding rate and post-seeding management efforts at each site.</li> <li>We found that no measure of seed source locality predicted the likelihood of plant establishment or abundance at restoration sites. However, sites sown with seed from further away, or from cooler and wetter climates, had a greater proportion of flowering individuals earlier in the season. Finally, sites with higher seeding rates had greater plant abundance, and post-seeding management of the restoration site increased the likelihood a species would establish by 36%.</li> <li>Overall, these results suggest that seed sourcing did not impact plant establishment or abundance in our system. However, using fewer local seed sources can alter flowering phenology.</li> <li>Our results suggest that tallgrass prairie restoration efforts should prioritize higher seeding rates, post-seeding management, and might expand the region seed sources are considered "local", though this could impact flowering phenology. Future research leveraging native seed producer records can help answer critical questions about restoration seed sourcing.</li> </ol>

opencc-zeroMar 2023View details →

zenodo40/100

Dynamic Drawings - restored interactive 3D visualizations

Dynamic Drawings - restored interactive 3D visualization Introduction: As indicated on the <a href="http://demo-brill.dans.knaw.nl">original project website</a> [1], the Dynamic Drawings in Enhanced Publications project “explored ways to enrich scientific papers with such visualizations, from authoring to publishing and archiving them. The project involved publisher Brill, researchers from HuygensING, the scientific data archive DANS and game developer from Wild Card. In nine months they worked collaboratively on various instruments or processes that have been described in 17th century texts.” Dynamic Drawings was "part of a joint venture of the University of Amsterdam (UvA), Vrije University Amsterdam (VU) and the KNAW to support research in the digital humanities that focuses on collaboration between research institutions, non-profit organizations (e.g. cultural heritage institutions) and private companies in the creative industry to develop innovative digital research methods and new modes of valorization of humanities knowledge" (Van den Heuvel et al., 2013). The project was a collaboration between a historian of science (Museum Boerhaave), a game developer (Wild Card), scientific programmers (DANS) and a publisher (Brill Publishers). More specifically, the collaboration led to six interactive visualizations. As indicated by Van den Heuvel et al. (2013), these visualizations were not merely illustrations, but interactive scholarly multimedia annotations. List of visualizations: <ol> <li>a Mill model devised by Agostino Ramelli (2013a)</li> <li>the mathematical optimizations of Fortifications (2013b)</li> <li>Rene Descartes' light refraction model (2013c)</li> <li>Swammerdam’s microscopic drawings (2013d)</li> <li>Early Modern educational materials on Surveying / Triangulation (2013e)</li> <li>an interactive Astrolabe (2013f)</li> </ol> However, the interactive applications that were made during Dynamic Drawings ceased functioning, due to the Unity plug-in for the Web which was not supported anymore. As the project took a "best practice" approach to the archiving of project resources, all original source files were archived and still available via the DANS EASY data repository (see links below). In 2020, these files were put under an open CC-0 license. Within the <a href="https://www.virtualinteriorsproject.nl">Virtual Interiors project</a> (2018-2022), the interactive models were restored and resurrected. Due to changes in the Unity editor in the meantime, some of the aspects of the models had to be reverse-engineered. This process will also be documented in an accompanying paper (Huurdeman, van den Heuvel, Posthumus, forthcoming). Project documentation: van den Heuvel, C. M. J. M., Hoogerwerf, M. L., Cocquyt, T., Gilissen, V., & Thijssen, M. (2013). Dynamic Drawings in Enhanced Publications. Eindrapport KNAW PPS project. Available via: <a href="https://pure.knaw.nl/portal/nl/publications/dynamic-drawings-in-enhanced-publications">https://pure.knaw.nl/portal/nl/publications/dynamic-drawings-in-enhanced-publications</a> Original data deposits from 2013 (DANS EASY): 1. Ramelli Mill: <ul> <li>Nagel, D., Cocquyt, T., (2013a): Animated, interactive 3D visualization of a corn mill, after a description by Ramelli [Data set]. DANS. <a href="https://doi.org/10.17026/dans-zzq-ymge">https://doi.org/10.17026/dans-zzq-ymge</a></li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56166/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56166/tab/1</a></li> </ul> 2. Fortification <ul> <li>Nagel, D., Cocquyt, T. (2013b): Interactive visualisation of fortification [Dataset]. DANS. <a href="https://doi.org/10.17026/dans-zkf-7wpa">https://doi.org/10.17026/dans-zkf-7wpa</a></li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56147/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56147/tab/1</a></li> </ul> 3. Descartes Refraction <ul> <li>Nagel, D., Cocquyt, T. (2013c): Interactive animation of Descartes Refraction [Dataset]. DANS. <a href="https://doi.org/10.17026/dans-zby-k8cz">https://doi.org/10.17026/dans-zby-k8cz</a> </li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56146/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56146/tab/1</a></li> </ul> 4. Swammerdam's microscopic drawings <ul> <li>Nagel, D., Cocquyt, T. (2013d): Interactive visualisation of Swammerdam’s microscopic drawings [Dataset]. DANS. <a href="https://doi.org/10.17026/dans-2an-4yaa">https://doi.org/10.17026/dans-2an-4yaa</a></li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56163/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56163/tab/1</a></li> </ul> 5. Surveying / Triangulation <ul> <li>Nagel, D., Cocquyt, T. (2013e): Interactive visualization of a surveying instruction [Dataset]. DANS. <a href="https://doi.org/10.17026/dans-xk8-5ag6">https://doi.org/10.17026/dans-xk8-5ag6</a></li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56165/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:56165/tab/1</a></li> </ul> 6. Astrolabe <ul> <li>Nagel, D., Cocquyt, T. (2013f): Interactive 3D visualization of an astrolabe [Dataset]. DANS. <a href="https://doi.org/10.17026/dans-28m-zann">https://doi.org/10.17026/dans-28m-zann</a></li> <li>Original metadata: <a href="https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:54390/tab/1">https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:54390/tab/1</a></li> </ul> Live demo's of the restored visualizations: <ol> <li>Ramelli Mill: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/ramellimill/&sa=D&source=docs&ust=1681207841386674&usg=AOvVaw3UVNnEy5nMyktY0BGUJ8CK">http://www.timelessfuture.com/apps/dynamicdrawings/ramellimill/</a></li> <li>Survey: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/survey/&sa=D&source=docs&ust=1681207841386791&usg=AOvVaw3tIKKZzFpv_V73O6nBr0Vm">http://www.timelessfuture.com/apps/dynamicdrawings/survey/</a></li> <li>Astrolabe: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/astrolabe/&sa=D&source=docs&ust=1681207841386837&usg=AOvVaw2D-U58C8l5XOFtjjfu8_9s">http://www.timelessfuture.com/apps/dynamicdrawings/astrolabe/</a></li> <li>Refraction: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/refraction/&sa=D&source=docs&ust=1681207841386877&usg=AOvVaw3MszPsh7LmSDU9GlXdc1V5">http://www.timelessfuture.com/apps/dynamicdrawings/refraction/</a></li> <li>Fortification: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/fortification/&sa=D&source=docs&ust=1681207841386918&usg=AOvVaw2OeEtgocrWYzfhDCUZef2-">http://www.timelessfuture.com/apps/dynamicdrawings/fortification/</a></li> <li>Swammerdam drawings: <a href="https://www.google.com/url?q=http://www.timelessfuture.com/apps/dynamicdrawings/swammerdam/&sa=D&source=docs&ust=1681207841386957&usg=AOvVaw2ElZVz2XkOzWd31Y9L8qz6">http://www.timelessfuture.com/apps/dynamicdrawings/swammerdam/</a></li> </ol> Repository file structure: Original research underlying the interactive visualizations can be found <a href="https://pure.knaw.nl/portal/nl/publications/dynamic-drawings-in-enhanced-publications">here</a>. In this dataset, the following files are provided for each visualization (#1 - #6): <ol> <li>Unity source files (updated Unity project files and source code) <ul> <li>Unity files and settings. Including source code (Assets/Scripts folder), 3D model elements in OBJ-format (Assets/Models folder), images and GUI images (Assets/Textures folder) and Unity scene (Assets/Scenes folder)</li> </ul> </li> <li>WebGL_application (exported version of the application) <ul> <li>HTML files, using <a href="https://www.khronos.org/webgl/">WebGL</a> technology. Exports from the Unity application, which can be uploaded to a web server</li> </ul> </li> <li>3D models (if available) <ul> <li>Exported (static) combined 3D models from the application, as <a href="https://www.khronos.org/gltf/">GLTF</a> and <a href="https://en.wikipedia.org/wiki/FBX">FBX</a> files. In addition, 3D model elements (in <a href="https://en.wikipedia.org/wiki/Wavefront_.obj_file">OBJ</a>-format) are included within the Unity source files (see above)</li> </ul> </li> <li>Documentation <ul> <li>Screen_recordings. For visual reference, screen recordings of the original Unity application</li> <li>Screenshots. For visual reference, screenshots of the original Unity application</li> </ul> </li> </ol> Further information regarding Ramelli Mill visualization (visualization #1): <ul> <li>The algorithms driving the mill behavior can be found in the file Ramelli_Mill/Unity_source_files/Assets/Scripts/Mill.cs (C# programming language).</li> <li>The documentation of the underlying algorithms is available in Van den Heuvel et al. (2013), Dynamic Drawings in Enhanced Publications (p.28-31): "The backbone of the enrichment [was] a dynamic algorithm, driving the 3D mill which was modeled after the engravings, and taking input from five sliders that adjust the variables." For further details about the original modeling choices for this algorithm, see that publication.</li> </ul> Further technical notes from the restoration process: <ul> <li>The Unity source files contain the editable assets and programming code used for creating the application. The development of technologies occurs at a rapid pace, as a result Unity frequently changes between versions, often necessitating changes to make the Unity source code compatible with a new version. The version in which the visualizations were created was Unity version 4.1.3f3, available in the Unity download archive, <a href="https://unity3d.com/get-unity/download/archive">https://unity3d.com/get-unity/download/archive</a>. The last working version in Windows/MacOS which the source files could be opened was 4.5.5. This version was then upgraded in steps to version Unity editor version 2019.4.40f1, while correcting occurring issues along the way.</li> <li>For scripting, Unity uses a standard programming language (C#), but the underlying names of functions supported by Unity change from time to time.  We could resolve these issues using available documentation of various versions of Unity (<a href="https://docs.unity3d.com/Manual/index.html">https://docs.unity3d.com/Manual/index.html</a>), and via unofficial answers to user questions posted on the Unity development forum (<a href="https://forum.unity.com/">https://forum.unity.com/</a>).</li> </ul> Usage instructions for Unity source files: <ul> <li>Download and install Unity LTS version 2019.4.40f1 (see: <a href="https://unity.com/releases/editor/archive">https://unity.com/releases/editor/archive</a>)</li> <li>Add the project to Unity via Unity Hub (Open > Add project from disk)</li> <li>Open the project</li> <li>In the Unity editor, choose the scene “Main” under project assets</li> <li>Run the application within the editor, or create a new build via File > Build settings. This way, custom versions of the application can be built for Windows, Mac, Linux or other platforms.</li> </ul>

opencc-zeroMar 2023View details →

zenodo40/100

Data from: A framework to apply trait-based ecological restoration at large scales

R scripts and data to run the proposed framework at Coutinho, A. G., Carlucci, M. B., Cianciaruso, M. V. (2023) A framework to apply trait-based ecological restoration at large scales. Journal of Applied Ecology.

opencc-by-4.0May 2023View details →

zenodo40/100

Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

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. 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.  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.

opencc-by-4.0Aug 2023View details →

zenodo40/100

Input raster datasets for Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

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.

opencc-by-4.0Aug 2023View details →

zenodo40/100

In silico data for: Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling

Directory layout and description for deposited data, scripts, and results associated with Folding correctors can restore CFTR post-translational folding landscape by allosteric domain-domain coupling Naoto Soya, Haijin Xu, Ariel Roldan, Zhengrong Yang, Haoxin Ye, Fan Jiang, Aiswarya Premchandar, Guido Veit, Susan P.C. Cole, John Kappes, Tamas Hegedus, and Gergely L. Lukacs   Two files are provided: <ol> <li>soya_md_trajectories.tar - This file contains the trajectories merged from the last part (450-500 ns) of the parallel simulations: md_450000_500000.xtc</li> <li>soya_insilico_data.zip - This file contains all other deposited files including input data, scripts, results files. The content of this file can be found below:</li> </ol> README.md - the content of this description homo - Homology modeling <ul> <li>run*.py, myloopmodel.py, and mymodel.py files are separated for technical reasons, for running model-building in parallel mode</li> <li>cftr-loop - Demonstrates the removal of the RI and seeling the break with loopmodeling</li> <li>mrp1 - Scripts and input files for human MRP1 homology modeling; the large unresolved loop in NBD1 was not modeled but sealed for MD; this required renumbering of the ouput</li> <li>mrp6 - Scripts, input, and output files for human MRP1 homology modeling; output: mrp6_human_closed.pdb; the selected CFTR and MRP1 models were the input for MD simulaitons; to see these energy minimized structures, please see the corresponding 'md' directory below.</li> </ul> md - Moldecular dynamics <ul> <li>The md_system_info.xlsx file contains the basic properties of simulation boxes</li> <li>MD parameter files: step6*.mdp for minimization and equilibration; step7_production.mdp for production run</li> <li>wordom.dat is the wordom configuration file</li> <li>cmap_mda.mp.py is a script for contact map calculation</li> <li>cftr-*, mrp1-* <ul> <li>the simulation system generated by CHARMM-GUI: step5_charmm2gmx.pdb</li> <li>the output gro file of parallel simulations (the last state of the sysmtems): md_[1-6].gro</li> <li>! the trajectory merged from the last part (450-500 ns) of the parallel simulations: trajectories md_450000_500000.xtc are in a separte file (soya_md_trajectories.tar) with the same directory structure</li> <li>the merged trajectory contains only the SOLU; the corresponding structure file: prot.pdb</li> <li>index.ndx</li> </ul> </li> </ul> figures <ul> <li>figure-3a <ul> <li>pdb files are the output of gmx rmsf</li> <li>pse file is saved visualization of the pdb files for PyMOL  </li> </ul> </li> <li>figure-3c-s4b <ul> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> <li>dri in file names and in scripts refers to 6ss  </li> </ul> </li> <li>figure-s3a <ul> <li>tmd1_structures.pse contains the structures for PyMOL</li> <li>Please see the Source Data file for plotting RMSF</li> <li>Contact map data are in the tmd1_wt.npy and tmd1_r170g.npy file  </li> </ul> </li> <li>figure-s3e <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures  </li> </ul> </li> <li>figure-s4a <ul> <li>Contains the calculated betweenness.txt files</li> <li>betweenness_plots.py for plotting</li> <li>wt_prot.pdb: required for plotting with resi thick-labels  </li> </ul> </li> <li>figure-s5c <ul> <li>PyMOL pse files to visualise the dynamics of NBD1/2 structures  </li> </ul> </li> <li>figure-s8d <ul> <li>Data for MRP1/ABCC1</li> <li>You can run color_all.tcl in VMD to reproduce the network communities in structural context; this is dependent on the .pdb and .vmd files also deposited in this directory</li> <li>Network community members (residues) are listed in the Word files</li> </ul> </li> </ul>

opencc-by-4.0Oct 2023View details →

zenodo40/100

UniFMIR: Pre-training a Foundation Model for Universal Fluorescence Microscopy Image Restoration

This repository contains the preprocessed dataset for [UniFMIR](https://github.com/cxm12/UNiFMIR/). All training and test data involved in the experiments are publicly available datasets. Licenses of the original dataset are applied. You can refer to the Github repository for details. * The 3D denoising/isotropic reconstruction/projection datasets can be downloaded from [Content Aware Image Restoration dataset](https://publications.mpi-cbg.de/publications-sites/7207/). `Projection_Flywing/train_data/my_training_data.npz` are generated according to the [CSBDeep](http://csbdeep.bioimagecomputing.com/doc/). * The SR dataset can be downloaded from [BioSR dataset](https://doi.org/10.6084/m9.figshare.13264793). The dataset is augmented according to the instructions in [DFCAN](https://github.com/qc17-THU/DL-SR/tree/main#train-a-new-model) and `my_training_data.npz` files are generated following [CSBDeep](http://csbdeep.bioimagecomputing.com/doc/datagen.html).  * The Volumetric reconstruction dataset are from [VCD-LFM dataset](https://doi.org/10.5281/zenodo.4390067).  The dataset is prepared according to the instructions in [VCD-Net](https://github.com/feilab-hust/VCD-Net). * DeepBacs dataset can be downloaded from [DeepBacs dataset](https://zenodo.org/record/6460867). We split the dataset into 5 folds for cross-validation. Shareloc dataset can be downloaded from [Shareloc dataset](https://zenodo.org/record/7234161).   The data paths should be as follows: ``` VCD/vcdnet/ CSB/DataSet/     Denoising_Planaria/     Denoising_Tribolium/     Isotropic/Isotropic_Liver/     Projection_Flywing/     BioSR_WF_to_SIM/DL-SR-main/dataset/     Synthetic_tubulin_gfp/     Synthetic_tubulin_granules/ DeepBacs/ Shareloc/ ```

opencc-by-4.0Oct 2023View details →

ClinicalTrials.gov40/100

Regenerative Collagen Scaffold for Breast Volume Restoration in Breast-Conserving Surgery

ClinicalTrials.gov study NCT07219316. IPD Sharing: YES. Countries: 1. Publications: 5.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov40/100

Vision Restoration With a Collagen Crosslinked Boston Keratoprosthesis Unit

ClinicalTrials.gov study NCT02863809. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

dryad40/100