Find research datasets worth reusing

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-12: model 11291 to 12291)

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-12: model 11291 to 12291) <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> Notes: 1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar). 2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3. 3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files. Developed by: Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain). Email contact: morteza.rasouli@upf.edu

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-8: model 7287 to 8287)

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-8: model 7287 to 8287) <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> Notes: 1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar). 2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3. 3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files. Developed by: Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain). Email contact: morteza.rasouli@upf.edu

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-15: model 14294 to 15294)

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-15: model 14294 to 15294) <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> Notes: 1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar). 2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3. 3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files. Developed by: Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain). Email contact: morteza.rasouli@upf.edu

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-13: model 12292 to 13292)

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-13: model 12292 to 13292) <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> Notes: 1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar). 2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3. 3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files. Developed by: Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain). Email contact: morteza.rasouli@upf.edu

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-16: model 15295 to 16295)

16807 thoracolumbar osteo-ligamentous spine virtual FE input files (part-16: model 15295 to 16295) <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 39MB.</li> <li>An excel file: "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model’s name.</li> <li>One video file: "how_to_replace_point_coordinates.mp4". It shows how you can replace point coordinates here to the mean FE input file "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> Notes: 1- Model number in "<a href="https://zenodo.org/api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: 10.5281/zenodo.7715658; stl.part01.rar to stl.part09.rar). 2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3. 3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files. Developed by: Morteza Rasouligandomani (Ph.D. student in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain). Email contact: morteza.rasouli@upf.edu

Primary raw observation input file for the Ring Laser Analysis program

This dataset is the primary input for the ring laser analysis program. It comprises a tab-delimited table, containing time information, the observed Sagnac frequency and a fairly large number of auxiliary measurements in order to compute the necessary raw data reductions.

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

opencc-by-4.0Sep 2023View details →

Input data for: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution

This Zenodo archive contains essential input datasets utilized in our <a href="https://doi.org/10.5194/essd-2023-112">research study</a> titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.Datasets Included:CoDEC (Coastal Dataset for the Evaluation of Climate Impact):<ul><li>This dataset is described in<a href="https://doi.org/10.3389/fmars.2020.00263"> Muis et al. (2020)</a></li><li>cf_esl folder: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point.</li><li>cf_tides folder: This folder holds data related to tidal elevation.</li><li>coor_coastal.nc: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.</li></ul><ol><li>HR (Hybrid Reconstructions):<ul><li>HybridRec_Upd0422.mat: This file contains data from the Hybrid Reconstructions dataset (<a href="https://doi.org/10.1038/s41558-019-0531-8">Dangendorf et al 2019</a>), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our <a href="https://doi.org/10.5281/zenodo.7771501">source code</a> named split_hr_dataset_to_stations.py.</li></ul></li></ol>We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.

opencc-by-4.0Sep 2023View details →

LOPT input files for Mg VII and Si VII

This entry contains input files for the LOPT atomic physics code. The results are described in the article "Updated reference wavelengths for Si VII and Mg VII lines in the 272--281 Angstrom range" by Dr. Peter R. Young (NASA Goddard Space Flight Center) that was submitted to The Astrophysical Journal.There are two sub-directories called mg7 and si7 for the two ions. In each there are three input files of the form:Mg7_FixLEV_EIS.txt - List of fixed levels in the atomic model. Mg7_lin_EIS.prn - List of wavelengths and uncertainties for the lines of the model. Mg7_lopt_EIS.par - The parameter input file for LOPTYou will need to install the LOPT code to use these files. See Kramida, A. E. 2011, Computer Physics Communications,236 182, 419.In each directory you will see two additional files that are the output wavelength and energy files generated by the author.Version 1.1: the input/output filenames in the Mg7_lopt_EIS.par were wrong in the previous version. This has been fixed. The input data are the same as before.

openCC (other)Sep 2023View details →

Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.

Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.

openCC (other)Apr 2024View details →

Delta smelt (Hypomesus transpacificus) life cycle model input data.

Synthesized data used for fitting delta smelt population dynamics models, essentially consisting of predictor variables (environmental conditions and indices of prey and predators) and response variables (abundance indices). Input data is sourced from a variety of both federal and California state government monitoring programs taking place within the San Francisco Estuary, California. These include California Department of Fish and Wildlife fish surveys, Interagency Ecological Program's Environmental Monitoring Program for zooplankton, California Department of Water Resources' Dayflow, and United States Geological Survey water monitoring data. The sourced data are recorded from sub-hourly to monthly time scales and at various spatial scales, aggregated at monthly or greater time scales using summary statistics (e.g. means) and are not spatially explicit but use spatial stratification approaches for statistic calculation as appropriate.

openCC (other)May 2024View details →

Bottom-up meets top-down: Leaf litter inputs influence predator-prey interactions in wetlands, 2011.

While the common conceptual role of resource subsidies is one of bottom-up nutrient and energy supply, inputs can also alter the structural complexity of environments. This can further impact resource flow by providing refuge for prey and decreasing predation rates. However, the direct influence of different organic subsidies on predator–prey dynamics is rarely examined. In forested wetlands, leaf litter inputs are a dominant energy and nutrient resource and they can also increase benthic surface cover and decrease water clarity, which may provide refugia for prey and subsequently reduce predation rates. In outdoor mesocosms, we investigated how inputs of leaf litter that alter benthic surface cover and water clarity influence the mortality and growth of gray treefrog tadpoles (Hyla versicolor) in the presence of free-swimming adult newts (Notophthalmus viridiscens), which are visual predators. To manipulate surface cover, we added either oak (Quercus spp.) or red pine (Pinus resinosa) litter and crossed these treatments with three levels of red maple (Acer rubrum) litter leachate to manipulate water clarity. In contrast to our predictions, benthic surface cover had no effect on tadpole survival while darkening the water caused lower survival. In addition, individual tadpole mass was lowest in the high maple leachate treatments, suggesting an interaction between bottom-up effects of leaf litter and topdown effects of predation risk that altered mortality and growth of tadpoles. Our results indicate that realistic changes in forest tree composition, which cause concomitant changes in litter inputs to wetlands, can substantially alter community interactions.

openCC (other)Oct 2025View details →

Detrital Inputs and Removal Treatments (DIRT) Experiment at the University of Michigan Biological Station, Pellston, MI (2004-2019)

Ecological research networks functioning across climatic and edaphic gradients are critical for improving predictive understanding of biogeochemical cycles at local through global scales. One international network, the Detrital Input and Removal Treatment (DIRT) Project, was established to assess how rates and sources of plant litter inputs influence accumulations or losses of organic matter in forest soils. DIRT employs chronic additions and exclusions of aboveground litter inputs and exclusion of root ingrowth to permanent plots at eight forested and two shrub/grass sites to investigate how soil organic matter (SOM) dynamics are influenced by plant detrital inputs across ecosystem and soil types. Across the DIRT network described here, SOM pools responded only slightly, or not at all, to chronic doubling of aboveground litter inputs. Explanations for the slow or even negative response of SOM to litter additions include increased decomposition of new inputs and priming of old SOM. Evidence of priming includes increased soil respiration in litter addition plots, decreased dissolved organic carbon (DOC) output from increased microbial activity, and biochemical markers in soil indicating enhanced SOM degradation. SOM pools decreased in response to chronic exclusion of aboveground litter, which had a greater effect on soil C than did excluding roots, providing evidence that root-derived C is not more critical than aboveground litter C to soil C sequestration. Partitioning of belowground contributions to total soil respiration were predictable based on site-level soil C and N as estimates of site fertility; contributions to soil respiration from root respiration were negatively related to soil fertility and inversely, contributions from decomposing aboveground litter in soil were positively related to site fertility. The commonality of approaches and manipulations across the DIRT network has provided greater insights into soil C cycling than could have been revealed at

openOpenNov 2005View details →

Patterns of and controls over nitrogen inputs by green alder (Alnus viridis spp. fruticosa) to a secondary successional chronosequence in interior Alaska I - N2 Fixation and Soil Temperature

We measured rates of nitrogen fixation by Alnus viridis spp. fruticosa and concurrent subcanopy soil temperature at BNZ LTER. To do so we utilized replicate (n=3/stage) stands of a seral sequence of successional stages maintained by the Bonanza Creek Long-Term Ecological Research program (BNZ LTER). At each of the 9 replicate stands we selected a total of 70 individual shrubs. During each of 7 sampling periods, 3 across the growing season of 1997 and 4 during 1998, we randomly selected 10 of 70 A. viridis spp. fruticosa at each replicate stand. We used acetylene reduction assays (ARA) to estimate rates of N2 fixation at each of the selected shrubs and concurrently measured soil temperature at each shrub. The attached database may be utilized to (1) elucidate seasonal trends in rates of N2 fixation by A. viridis spp. fruticosa across a boreal forest chronosequence and (2) investigate soil temperature controls over rates of N2 fixation. It may also be used to statistically analyze differences between years, successional stages and replicates within successional stage in both rates of ARA and temperature. We developed this database to describe seasonal trends of rates of nitrogen fixation by A. viridis spp. fruticosa across a boreal forest chronosequence and to elucidate soil temperature controls over rates.

openOpenMar 2009View details →

Patterns of and controls over nitrogen inputs by green alder (Alnus viridis spp. fruticosa) to a secondary successional chronosequence in interior Alaska II - Soil Physical and Chemical Properties

In September of 1999 we collected soil cores to identify stage, replicate stand, canopy, and soil horizon patterns of soil physical (color, bulk density, pH) and chemical (N, C, P) parameters.

openCustomJan 2020View details →

Litter decomposition in quadrat treatments along elevation gradient for canopy herbivore input study at the Coweeta Hydrologic Laboratory from 1997 to 1999

Decomposition is frequently measured using litter bags containing known amounts of litter. A set of litter bags can be sampled over time and the weight loss which is measured serves as an index of decomposition. By measuring litter breakdown rate (decomposition) of the same species of litter along the elevation gradient, we could measure variation among the different elevations due to our treatments and elevation effects. Treatments included frass additions, thrufall additions, greenfall exclusion, all litter excluded, and controls.

opencc-by-4.0Dec 2019View details →

Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"

Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665 There are 2 files in this upload: 1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife. 2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.

opencc-by-4.0Dec 2018View details →

Database on the Performance of Current Agro-Ecological Farming Systems (AEFS) as an Input to the Modelling in WP4

This database contains farm data (e.g. yields) and results (indicators) from assessments with the three decision support tools in the UNISECO case studies: SMART (www.fibl.org/en/themes/smart-en.html), Cool Farm Tool (coolfarmtool.org) and COMPAS (www.thuenen.de). This version (2.0) was developed as a benchmark for the assessment of exemplary cases of how the core dilemmas of agro-ecological transitions may be overcome at farm level and as an input to the modelling at territorial level in WP4. This database was created in the course of the H2020 project UNISECO. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 773901.

opencc-by-4.0Jan 2020View details →

Behavioural Simulator Matsim Input Data

Behavioural simulator requires 4 input data files. Each file contains the following information: 1. Network.xml file contains road network information based on no of lanes, speed limits, vehicle access details derived from open street maps. 2. Plan.xml file contains synthetic population along with activity-travel information. These activity-travel patterns are output generated from activity-based models.  3. Schedules.xml file have information about public transport schedules with stops details, timetables etc drived from GTFS data 4. Vehicels.xml file is comprised of Public Transport Fleet information e.g no of buses.