Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Data part of the manuscript Anaerobic methanotrophy is stimulated by graphene oxide in a brackish urban canal sediment

We surveyed three canals in the city of Amsterdam (Netherlands) for it methane emissions and potential to filter methane through anaerobic oxidation of methane in the canal sediment. To unravel the mechanisms involved we characterised the sediment geochemically. All data present in the manuscript is available in the Excel file.

opencc-by-4.0May 2022View details →

Main sediment profiles and 3D-pdf from archaeological wetland excavation at Immensee-Dorfplatz

These are a 3D model and the longest and most representative sediment profiles through the waterlogged Late Neolithic lakeside site of Immensee-Dorfplatz (SZ).

opencc-by-4.0Jun 2023View details →

June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)

June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) Description Supplementary dataset to: Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only File descriptions <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> References Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a> **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>

opencc-by-4.0Jun 2023View details →

Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery

Doodleverse/Segmentation Gym Residual Unet models for 2-class (alluvial sediment, other) segmentation of RGB aerial orthomosaic imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify subaerial alluvial sediment (sand, gravel, etc) in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC. Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Jun 2023View details →

Geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna, Italy.

This dataset contains all raw geochemical data of bottom sediments from a network of drainage canals located in the low-lying coastal area of Ravenna. The dataset is divided in three separated excel worksheets:  - Focus Area. Sediment composition of the 21 sediment samples collected in 2022 in the Focus Area. Refer to Figs. 1 and 2 in the manuscript Giambastiani et al., 2024 for the sample locations. Listed are also other information related to sampling, such as depositional facies (BR: beach ridge deposits; IF: Interfluvial floodplain deposits), distance from the sea, altimetry, amount of fertilizer applied based on the land use, and EC of drainage water.  The sediment samples were collected in March 2022 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by the authors. - LRC, Land Reclamation Consortium. PTEs composition of the sediment samples of the Land Reclamation Consortium dataset. Refer to Fig. 1 and 2 in the manuscript Giambastiani et al., 2024 for the location. Listed are also other information related to sampling, such as distance from the sea, altimetry, and amount of fertilizer applied based on the land use.  The sediment samples were collected since 2010 along the drainage system of the lowlying coastal aquifer of Ravenna (Italy) by The Land Reclamation Consortium of Romagna (Italy). No other uses apart from scientific purpose is allowed without notice to the authors. - Wells. Physical and chemical groundwater parameters of 4 wells localted within the Focus Area. Refer to Fig.2  in the manuscript Giambastiani et al., 2024 for the location.  Data were collected during previous studies by Greggio et al. (2020) and reprocessed to obtain vertical profiles of EC, pH, Eh, and chemical concentrations along the coastal aquifer depth. More informations regarding the source, ownership, collection methodologies and analytical techniques are in Giambastiani et al., 2024.

Interpolation of the median grain size of the first 2 cm sediment layer in the former saltworks of Salin de Giraud in 2017

Sediment samples were collected in the summer of 2017 over the entire study area at 500 m spacing and in the channels. Grain size analysis of the collected sediment samples was conducted using a Malvern Mastersizer 2000© laser beam grain sizer. The median grain size (d50 in µm) at each sample location was then interpolated over the entire study area. Interpolation was made with the SAGA-GIS software (version 7.9.0). According to the cross-validation error, the best method for the D50mm interpolation was the Modified Quadratic Shepard. The 10-fold validation provided an R² of 0.93, an NMRSE of 24.5, an RMSE of 83.9, an MRE of 7039 with the fit set to “node”, the quadratic neighbours and weighting neighbours set to 50 and the spatial resolution was set to 10 m. The resultant interpolation map was then categorized following the nomenclature of Blott and Pye (2001) provided in the file style_sediment_map.qml.

Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery

Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC. Classes: {0=other, 1=water, 2=sediment, 3=large woody debris / driftwood}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

Doodleverse/Segmentation Gym SegFormer models for 2-class (other, sediment) segmentation of RGB aerial orthomosaic imagery

Doodleverse/Segmentation Gym SegFormer models for  2-class (other, sediment)  segmentation of RGB aerial orthomosaic imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC. Classes: {0=other, 1=sediment}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0May 2023View details →

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. These Segformer model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following datasets**: <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> and ***https://doi.org/10.5281/zenodo.8011926. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall <ul> <li>v5: .94, .90, .64, .87</li> <li>v6: .93, .89, .63, .87</li> <li>v7: .92, .88, .61, .84</li> <li>v8: .93, .89, .63, .87</li> <li>v9: .92, .88, .62, .85</li> <li>v10: .93, .89, .63, .86</li> </ul>   File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to this set of Residual UNets: Buscombe, Daniel. (2022). Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6950472 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 MNDWI images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 MNDWI images of coasts. Models have been created using Segmentation Gym* using the following datasets ** https://zenodo.org/record/7384263 and ***: https://doi.org/10.5281/zenodo.7335647. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall     v2: 0.808, 0.7309,    0.47864, 0.656     v3: 0.809, 0.7302, 0.4982, 0.664 File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to these sets of Residual UNets:     https://zenodo.org/record/7352850     https://zenodo.org/record/7557080 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384263 ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 NDWI images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 NDWI images of coasts. Models have been created using Segmentation Gym* using the following datasets ** https://zenodo.org/record/7384263 and ***: https://doi.org/10.5281/zenodo.7335647. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall <table> <tbody> <tr> <td>0.896016693115234</td> <td>0.832759195999637</td> <td>0.565748652153519</td> <td>0.806139409136944</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>0.906008201175266</td> <td>0.847625161837392</td> <td>0.593821675882991</td> <td>0.819790462192222</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>0.903999212053087</td> <td>0.844255821722932</td> <td>0.577444030164045</td> <td>0.813646408575</td> </tr> </tbody> </table>   File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to these sets of Residual UNets: https://zenodo.org/record/7557072 https://zenodo.org/record/7352859 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** https://zenodo.org/record/7384263 ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

opencc-by-4.0Dec 2021View details →

Supplementary data for "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic sediment bacterial assemblages of an intermittent stream"

Supplementary data for the Pozzi et al. paper entitled "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic microbial assemblages and a tracking of exogenous bacterial taxa in a peri-urban intermittent stream". # Created by Dr Adrien C. MEYNIER POZZI on June, 29th, 2023 # Part of DOmic research project funded by the Agence de l’Eau - Rhône Méditerranée Corse [AE-RMC, Project 2020 0702 DOmic, 2020-2023], and of the DOmic extension funded by the EUR H2O'Lyon [ANR-17-EURE-0018] of Université de Lyon # Part of the Chaudanne river long-term experiment site belonging to the Observatoire de Terrain en Hydrologie Urbaine (OTHU) # Part of the work conducted in the team on Opportinistic Bacterial Pathogen in the Environment (BPOE) led by Dr. Benoit Cournoyer # Samples were obtained in 2 campaigns, corresponding to periods before (2010-2011) or after (2018) the implementation of the 91/271/EEC European Directive that limited Combined-Sewer Overflow (CSO) discharges to the Chaudanne river # Samples consisted in surface water, benthic and hyporheic sediments taken in run, riffle and pool geomorphologic features, either upstream or downstream the CSO outlet, plus positive and negative controls <table> <tbody> <tr> <td>Metadata. Name and description of data tables provided as supplementary information</td> </tr> <tr> <td>Data Name</td> <td>Description</td> </tr> <tr> <td>Data S1. River hydrology variables and hydraulic gradients at surveyed transects</td> <td>Array to describe the hydrologic variables and gradients at the studied transects. Top line is header, second line is metadata for each recorded variable, and third line is the unit of the variable, if any.</td> </tr> <tr> <td>Data S2. Environmental variables (water physical-chemistry, nutrients, FIBs, MTEs, PAHs) with metadata</td> <td>An array to list environmental variables for all true samples (n=90) included in the study. Sample identifiers and dates are provided. First 8 rows list the CAS number, SANDRE number, unit, method, limit of quantification and norm  for each variable, if any.</td> </tr> <tr> <td>Data S3. Hydrological indices and synthetic variables computed with ClustOfVar</td> <td>Hydrological indices computed for the river flow, precipitations and CSO overflows computed over a 3-week period preceding each sampling date.</td> </tr> <tr> <td>Data S4. Discharge events selected to compute CSO dilution ratios</td> <td>An array to describe CSO events included for the computation of the CSO dilution ratio (SI Data 6A) together with 6 tables and 3 figures (SI Data 6B to 6J) describing the CSO event ratio all year round over the studied period, as well as for events that occurred before or after the CSO was modified and during low flow or high flow season. In SI Data 6A, top line is header and second line is metadata for each recorded variable.</td> </tr> <tr> <td>Data S5. Raw environmental matrix for use in R</td> <td>An array to list experimental design and environmental variables for all true samples and controls. Several environmental variables were synthetized using the ClustOfVar method (Chavent et al (2012) 10.18637/jss.v050.i13). Format is directly usable in R software.</td> </tr> </tbody> </table>

opencc-by-4.0Sep 2024View details →

River Sediment Database-Amazon (RivSed-Amazon)

The River Sediment Database-Amazon (RivSed-Amazon) database contains surface suspended sediment concentrations (SSC) derived from Landsat 5, 7, and 8 Level 1 Collection 1 surface reflectance from all rivers in the Amazon River Basin that are ~60 meters wide or greater. SSC represent spatially integrated "reach" median concentrations over the footprint of SWOT River Database (SWORD, Altenau et al., 2021) centerlines (median reach length = 10 km) where high quality river water pixels were detected within each Landsat image from 1984-2018.  The methods used to produce this database were initially developed in the following publications: <ul> <li>Gardner, J., Pavelsky, T. M., Topp, S., Yang, X., Ross, M. R., & Cohen, S. (2023). Human activities change suspended sediment concentration along rivers. Environmental Research Letters. <a href="https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8">https://iopscience.iop.org/article/10.1088/1748-9326/acd8d8</a> and </li> <li>Gardner et al. (2020). The color of rivers. Geophysical Research Letters.  <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088946</a></li> </ul> The publication associated with RivSed-Amazon is in review. Files: 1) Metadata (rivSed_Amazon_metadata_v1.01.pdf): Description key data files associated with this repository.  2) RiverSed (RiverSed_Amazon_v1.1.txt). Table of  SSC  and associated data that is joinable to SWORD based on the ""reach_id". 3) Shapefile of river centerlines over South America to which the reflectance data can be attached (SWORD_SA.shp). 4) Shapefile of the reach polygons associated with SWORD_SA over the Amazon Basin. (reach_polygons_amazon.shp). 5) SSC-Landsat matchup database with extended metadata on locations and in-situ data (train_full_v1.1.csv). 6) The final training data used to build the xgboost machine learning model (train_v1.1.csv). 7) The xgboost model that can make SSC predictions over inland waters in USA using Landsat bands/band combinations (tssAmazon_model_v1.1.rds and .rda). The model can only be loaded and used in R at this time. 8) The correction coefficients applied to Landsat 5 and 8 to harmonized surface reflectance across Landsat 5,7,8 and over all bands to enable time series analysis.

opencc-by-4.0Aug 2024View details →

Concentrations of methane, sulfate and lipid biomarkers and carbon isotope values oof lipids in the sediments from the outer Laptev Sea

The dataset contains the concentrations of methane, sulfate and microbial lipid biomarkers, and the carbon isotope composition of lipids in the sediment collected from the SWERUS-C3 expedition in 2014. The core sediment samples were from stations 13, 14 and 23 in the outer Laptev Sea. The field investigation reveals it is a methane seep area.

openCC0Mar 2022View details →

Sediment, C, N, and P Concentrations and Burial Rates in Three Southwestern Ohio Retention Ponds: 2006-2019

These datasets correspond to Rogers et al (2022) “Temporal patterns in sediment, carbon, and nutrient burial in ponds associated with changing agricultural tillage” published in Biogeochemistry (DOI: 10.1007/s10533-022-00916-w). We sampled three retention ponds in southwest Ohio in 2019 to compare sediment, carbon, nitrogen, and phosphorous burial rates to those calculated in 2006 to see the effect a watershed-wide shift to conservation tillage and to estimate the value of the ecosystem services these ponds provide via sequestration. Two methods were used to calculate burial rates: simple mean and spatially explicit. All datasets use simple mean except for the data set named “Spatially Explicit Sediment C N P Burial in SW OH Retention Ponds 2019”, which uses the spatially explicit method. For more information, please refer to our paper.

openCC (other)Mar 2022View details →

Summer 2017 porewater and sediment geochemistry data at Second Creek, a sulfate-impacted riparian wetland in northeast Minnesota

Water and sediment chemistry data were collected over the summer and fall of 2017 at Second Creek, a riparian wetland study site near Aurora, MN, to understand sulfur and methane processes. Porewaters were collected with two distinct methods “peepers” (multi-chambered equilibrium dialysis samplers) that allow for high vertical resolution but 2-3 week averaged temporal resolution, and rhizon samplers that enable instantaneous temporal resolution but have lower spatial resolution. Porewaters were analyzed for dissolved cations, anions, sulfide, methane, iron(II)/iron(III), and pH. Sediment cores were analyzed for acid volatile sulfide, and sulfur and iron speciation via X-ray absorption spectroscopy.

openCC (other)Jun 2022View details →

Geochemical data from sediments and porewaters from ferruginous and meromictic Brownie Lake, Minnesota, U.S.A.

The dataset is comprised of analyses of sediment cores and sediment trap samples from ferruginous and meromictic Brownie Lake, Minnesota, U.S.A from January 2018 through February 2021. The dataset includes bulk sediment characteristics including water content, grain size, major and minor elements. Voltammetric scans were collected on porewaters and lake waters. Sediment porewaters were analyzed for pH, total alkalinity, ferrous iron, and dissolved sulfur species contents. Sediment samples were maintained under the exclusion of oxygen for analysis by synchrotron-based X-ray absorption spectroscopy.

openCC (other)Jan 2023View details →

Experimental microcosm incubations assessing the effect of hypoxia on aqueous iron and organic carbon, pH, sediment organic carbon, and sediment iron-bound organic carbon

To assess the effect of changing oxygen concentrations on coupled carbon and iron cycling in freshwater ecosystems, we performed 6-week microcosm incubations. Incubations were inoculated with sediment and water from Falling Creek Reservoir, Vinton, VA, USA. We started the experiment with 102 microcosms split evenly into oxic and hypoxic treatments. After two weeks, we switched the treatment of approximately half of the remaining microcosms, generating a total of four oxygen regimes: hypoxic, oxic, hypoxic to oxic, and oxic to hypoxic. We sampled the microcosms destructively approximately twice per week, collecting aqueous samples for total and dissolved carbon and iron, as well as sediment samples for organic carbon and iron-bound organic carbon analysis. Iron-bound organic carbon was determined using citrate-bicarbonate-dithionite extractions.

openCC (other)Dec 2022View details →

Porewater sulfide, sediment sulfur, and organic matter data in West Falmouth Harbor, 2017-2019