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1,542 results for “Degradation”

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zenodo40/100

Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"

Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a> Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.   Break-in cycles: Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.   Ageing Conditions: <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge. For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.   Reference Performance Tests (RPTs): All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures. Long RPT procedure: <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> Short RPT procedure: <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol>   Extracted Data - Main One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes: Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to. Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles. Ageing Set Start Date/ End date: The date that each ageing set began/ ended. Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending. Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C. Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah. Energy Throughput: as with "charge throughput", but for energy. Units: Wh. C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh. C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh. 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.   Extracted Data - Degradation Modes: Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain: Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode. Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity. DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL. RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.   Timeseries data from RPTs: Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.   Jupyter Notebook: A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.   Notes: A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Identifying mountain permafrost degradation by repeating historical ERT-measurements - supplement

Ongoing global warming affects the degradation of mountainous permafrost. Permafrost thawing impacts landform evolution, reduces fresh water resources, enhances the potential of natural hazards, and thus has significant socio-economic impact. Electrical resistivity tomography (ERT) has been widely used to map the ice-containing permafrost by its resistivity contrast compared to the surrounding non-frozen medium. We analyse the temporal changes in the resistivity distribution by comparing historical with recently measured ERT profiles. Three periglacial landforms (two rock glaciers and one talus slope) are surveyed in the Swiss and Austrian Alps by repeating historical field campaigns after periods of 10, 12, and 16 years, respectively. The resistivity values have been significantly reduced concerning ice-poor permafrost at all study sites. Interestingly, resistivity values related to ice-rich permafrost in the studied active rock glacier partly increased during the studied time period. To explain this apparent contradictory (in view of observed increase) observation, geomorphological circumstances, such as the relief and creeping behaviour of the active rock glacier, are discussed. Additional remote sensing data indicates an increased velocity in and around the active part with increased resistivity. The present study highlights alpine permafrost degradation resulting from ever-accelerating global warming.

opencc-by-4.0Nov 2022View details →

zenodo40/100

The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure

Dataset and Python code complementing the publication  Kaiser, S.; Boike, J.; Grosse, G.; Langer, M. The Potential of UAV Imagery for the Detection of Rapid Permafrost Degradation: Assessing the Impacts on Critical Arctic Infrastructure. Remote Sens. 2022, 14, 6107. https://doi.org/10.3390/rs14236107 <ul> <li>AROSICS.zip contains the orthomosaic of 2018 shifted to 2019 with the AROSICS algorithm. The .txt file contains the x-/y-shift in map units [m].</li> <li>CC_DistancePointClouds.zip contains the distance point clouds as calculated via Multiscale Model to Model Comparison (M3C2 after Lague et. al, 2013) at each post-processing level (I-IV) and the validation.</li> <li>CC_PointCloudProcessing.zip contains the point clouds at post-processing levels II-IV.</li> <li>ODM_Orthomosaics.zip contains the orthomosaics of 2018 and 2019 as processed in WebODM (based on OpenDroneMap).</li> <li>ODM_PointClouds.zip contains the raw point clouds of 2018 and 2019 (post-processing level I) as processed in WebODM (based on OpenDroneMap).</li> <li>PointCloudStatistics.zip contains the M3C2 distance statistics at each post-processing level (I-IV) and the validation for the whole point cloud and the two subsets.</li> <li>Python_ChangeDetection.zip contains the Python (v 3.6) script for calculating the displacement vectors Dx, Dy, Dz for each distance point cloud, rasterizing the attribute "vertical displacement (Dz)" of the distance point cloud with the highest accuracy (post-processing level IV), applying a Sobel edge detection filter to highlight high image gradients and clustering the image into two categories: change (high image gradient) and no change (low image gradient). Needed data input is CC_DistancePointClouds.zip.</li> <li>Subsets.zip contains shapefiles of the two subsets.</li> </ul>

opencc-by-4.0Dec 2022View details →

zenodo40/100

Data and code from paper: The carbon sink of secondary and degraded humid tropical forests

This repository contains the data and code produced for the following paper: Title: The carbon sink of recovering secondary and degraded humid tropical forests Contact: Viola Heinrich (viola.heinrich@bristol.ac.uk) Please note: <ul> <li> throughout repository where files include reference to: <...congo_basin...> this refers to the Central Africa region as it is termed in the main paper.</li> <li>the code has not been amended for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. </li> </ul> The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in R (v3.6) for which the code and output datasets are available here. To access the code used in Google Earth Engine that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4  This repository contains the following zipped folders: <ul> <li>data_folder: this folder contains further folders with all the data produced for this paper.</li> </ul> <ol> <li>Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "regrowthModel_..RDS. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.</li> <li>Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "regrowthModel_..RDS) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. </li> <li>Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.</li> <li>Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.</li> <li>Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.</li> <li>Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. </li> </ol> <ul> <li>code_folder.zip: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. <ul> <li>this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. </li> </ul> </li> </ul> Data and Code usage: When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository.  Further source data in .xlsx format were also submitted with the main manuscript. If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)

opencc-by-4.0Nov 2022View details →

zenodo40/100

Ultrasound-driven degradation data for GenX, PFOA and PFOS at different ultrasonic frequencies and power densities

The spreadsheet contains the individual and mixture degradation data for three different PFAS type representatives: PFOA, PFOS, and GenX. All PFAS stock solutions were prepared in ultrapure water. The data was generated by sampling from 500 mL batches of PFAS stock solutions irradiated with ultrasound at predetermined time intervals. The document contains separate tabs for the individual degradation of the 3 PFAS, as well as the mixture degradation data. In addition to PFAS concentrations, fluoride concentrations measured with an ion-selective electrode are shared.

opencc-by-4.0Jan 2023View details →

zenodo40/100

Degradation rates of GrInHy2.0 HTE modules

Average degradation rates in mOhm*cm²/kh of the SOEC modules used in GrInHy2.0 plant, averaged over the runtime of the respective modules. There are 8 modules. Index "0" refers to the modules with initial stacks that were exchanges due to quality issues. Index "1" refers to replacement stacks with quality approved stacks. Runtime of initial stacks was 600 ... 9,000 h, while replacement stacks runtime was 5,000 ... 11,000 h.

opencc-by-4.0Feb 2023View details →

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

Multi-decadal stability of fish productivity despite increasing coral reef degradation

1. Under current trajectories, it is unlikely that the coral reefs of the future will resemble those of the past. As multiple stressors, such as climate change and coastal development, continue to impact coral reefs, understanding the changes in ecosystem functioning is imperative to protect key ecosystem services. 2. We used a 26-year dataset of benthic reef fishes (including cryptobenthic fishes) to identify multi-decadal trends in fish biomass production on a degraded coral reef. We converted fish abundances into estimates of community productivity to track the long-term trend of fish biomass production through time. 3. Following the first mass coral bleaching event in 1998, the abundance, standing biomass, and productivity of fish communities remained remarkably constant through time, despite the occurrence of multiple stressors, including extreme sedimentation, cyclones, and mass coral bleaching events. Species richness declined following the 1998 bleaching event, but rebounded to pre-bleaching levels and also remained relatively stable. 4. Although the species composition of the communities changed over time, these new community configurations still maintain a steady level of fish biomass production. While these highly dynamic and increasingly degraded systems can still provide some critical ecosystem functions, it is unclear whether these patterns will remain stable over future decades.

opencc-zeroMar 2023View details →

zenodo40/100

FTIR dataset from the article "Resistance to Degradation of Silk Fibroin Hydrogels Exposed to Neuroinflammatory Environments"

The attached archive encounters the FTIR data used for the calculation of the β-sheet content of the silk fibroin hydrogels in the in-vitro experiments. Such data is used in main Figures 2c and 2d, 4a and 7a as well as Supplementary Figure 2a of the associated article.   The acquisition of the files has been performed as indicated below: <ul> <li>Equipment: Thermo Scientific™ Nicolet™ iS™ 5 Spectrometer (Thermofisher, United States)</li> <li>Acquisition Software: OMNIC 9.2.86 (Thermofisher, United States)</li> <li>Number of Scans: 64</li> <li>Data Spacing: 0.482 cm -1</li> </ul>   The files have been reported as raw complete spectrum including the absorbances in the wavenumbers from 600 to 1500 cm -1 in the format of “.SPA”. In the following lines, we provide examples of software to process and analyze the spectra files included in the database. <ul> <li>Python version 3.8 to 3.10 upon the availability of the “SpectroChemPy” (Travert and Christian, 2023) function.</li> <li>Matlab R2016a and newer upon the availability of the “LoadSpectra” (Oldenburg, 2023) function.</li> <li>OMNIC 9 (Thermofisher, United States).</li> <li>Essential FTIR (Operant LLC, United States).</li> </ul>   Provision of the data in other formats is available upon request. Inquiries may be directed to <a href="mailto:daniel.gonzalez@ctb.upm.es?subject=FTIR%20Samples%20Format">Daniel González-Nieto</a> or <a href="mailto:mahdi.yonesi@ctb.upm.es?subject=FTIR%20Samples%20Format">Mahdi Yonesi</a>. References: OLDENBURG, K. 2023. LoadSpectra. MATLAB Central File Exchange. TRAVERT, A. & CHRISTIAN, F. 2023. SpectroChemPy, a framework for processing, analyzing and modeling spectroscopic data for chemistry with Python. Github.

opencc-by-4.0May 2023View details →

zenodo40/100

Enhanced diffusion barrier layers for avoiding degradation in SOFCs aged for 14000 h during 2 years

The dataset contains all the data used for the publication called Enhanced diffusion barrier layers for avoiding degradation in SOFCs aged for 14000 h during 2 years, Bernadet et al., Journal of Power Sources, 555, 2023, 232400 (10.1016/j.jpowsour.2022.232400) as well as for the supplementary information. Data is labeled with the figure number-letter and sample type. All the figures related to the nano-XRF data (Fig4, FigS1-4) were obtained from the same dataset, shared for Fig4 and FigS1.

opencc-by-4.0Nov 2022View details →

zenodo40/100

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation   Description: The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.   cloud_fraction[float]: Description: Effective cloud fraction derived from GOME-2 Level1B product. Units: none latitude[float]: Description: Pixel center latitude. Units: degrees N longitude[float]: Description: Pixel center longitude. Units: degrees E latitude_bounds[float]: Description: Latitude of the boundary corners for each pixel. Units: degrees N longitude _bounds[float]: Description: Longitude of the boundary corners. Units: degrees E SIF_740[float]: Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window. Units: mW m-2 nm-1 sr-1 SIF_daily [float]: Description: SIF signal at 740nm with correction of day-length. Units: mW m-2 nm-1 sr-1 Sigma_i[float]: Description:  The squre of single retrieval error of SIF_740. Units: (mW m-2 nm-1 sr-1)2 Solar_zenith_angle [float]: Description:  Solar zenith angle. Units: degrees Solar_azimuth_angle [float]: Description: Solar azimuth angle. Units: degrees Viewing _zenith_angle [float]: Description: Viewing zenith angle. Units: degrees Viewing_azimuth_angle[float]: Description: Viewing azimuth angle. Units: degrees chi2[float]: Description: The reduced chi-square value calculated based on the the fitting residuals. Units: None Rad_NIR[float]: Description: The average radiance within the 735~758 nm window Units: mW m-2 nm-1 sr-1 ps_NIR[float]: Description: The average reflectance within at around 680 nm (). Units: None ps_red[float]: Description: The average reflectance within the 665~680 nm window Units: None NDVI[float]: Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm). Units: None QA[int]: Description: Quality_flag. 0= Bad (ineffective original data) 1= Good (passed all quality-filtering criteria) 2= Good and the cloud fraction is lower than 0.3 Units:None

opencc-by-4.0Jul 2023View details →

zenodo40/100

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation

TCSIF: A temporally consistent global GOME-2A SIF dataset with correction of sensor degradation   Description: The global monthly GOME-2A SIF dataset (2007–2021) with correction of temporal degradation. The corrected global GOME-2 SIF dataset can be obtained in two types. The daily level2 dataset is provided in hdf5 format(compressed in the zip files named "{Year}{Quater}.zip"). The name of the hdf5 files was SIF_daily_YYYYMMDD.h5, YYYY, MM, and DD represent the year, month, and date, respectively. The level3 datasets which were aggregated monthly from the level2 dataset, have a spatial resolution of 0.5°and were saved in TIFF format in chronological order from 2007 to 2021 (compressed in the file "Level3.zip"). The name of the files was SIFpar_evi_monthly _YYYYMM.tif, where SIF was product type, par, and evi represented upscaled parameters, monthly represented temporal scale, YYYY and MM was the year and month, respectively. The SIF output was stored in the hdf5 files along with other variables of interest for further processing and visualization. See the appendix for the structure of the hdf5 file.   cloud_fraction[float]: Description: Effective cloud fraction derived from GOME-2 Level1B product. Units: none latitude[float]: Description: Pixel center latitude. Units: degrees N longitude[float]: Description: Pixel center longitude. Units: degrees E latitude_bounds[float]: Description: Latitude of the boundary corners for each pixel. Units: degrees N longitude _bounds[float]: Description: Longitude of the boundary corners. Units: degrees E SIF_740[float]: Description: SIF signal at 740nm retrieved using the 735–758 nm fitting window. Units: mW m-2 nm-1 sr-1 SIF_daily [float]: Description: SIF signal at 740nm with correction of day-length. Units: mW m-2 nm-1 sr-1 Sigma_i[float]: Description:  The squre of single retrieval error of SIF_740. Units: (mW m-2 nm-1 sr-1)2 Solar_zenith_angle [float]: Description:  Solar zenith angle. Units: degrees Solar_azimuth_angle [float]: Description: Solar azimuth angle. Units: degrees Viewing _zenith_angle [float]: Description: Viewing zenith angle. Units: degrees Viewing_azimuth_angle[float]: Description: Viewing azimuth angle. Units: degrees chi2[float]: Description: The reduced chi-square value calculated based on the the fitting residuals. Units: None Rad_NIR[float]: Description: The average radiance within the 735~758 nm window Units: mW m-2 nm-1 sr-1 ps_NIR[float]: Description: The average reflectance within at around 780 nm. Units: None ps_red[float]: Description: The average reflectance within the 665~680 nm window Units: None NDVI[float]: Description: Calculated by the TOA reflectance at red band (around 680 nm) and near-infrared band (around 780nm). Units: None

opencc-by-4.0Jul 2023View details →

zenodo40/100

Degraded Librispeech

Degraded Librispeech includes 34110 degraded speech samples obtained from 2650 clean speech sources extracted from Librispeech. DEGRADATIONS <ul> <li>Background noise <ul> <li>0, 8, 15, 25, 40 dB</li> </ul> </li> <li>Clipping <ul> <li>5, 10, 25, 40, 60 % of waveform samples</li> </ul> </li> <li>Opus <ul> <li>8, 16, 32, 64, 128 kbps</li> </ul> </li> <li>mp3 <ul> <li>8, 16, 32, 64, 128 kbps</li> </ul> </li> </ul> DETAILS Loudness is normalized using EBU R 128.  You can extract labels regarding the degradation type and intensity levels from the filenames. Degraded Librispeech has been used to develop NOMAD (Non-matching audio distance), a non-matching reference audio quality metric. NOMAD can also be used as a waveform generation loss function to improve speech quality, e.g., speech enhancement. References: NOMAD metric (ICASSP 2024): Ragano, Alessandro, Jan Skoglund, and Andrew Hines: NOMAD: Unsupervised Learning of Perceptual Embeddings for Speech Enhancement and Non-matching Reference Audio Quality Assessment <a href="https://arxiv.org/abs/2309.16284" target="_blank" rel="noopener">[Paper],</a><a href="https://github.com/alessandroragano/nomad" target="_blank" rel="noopener">[Code]</a> Original Librispeech dataset: Panayotov, Vassil, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. "Librispeech: an asr corpus based on public domain audio books." In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 5206-5210. IEEE, 2015.

opencc-by-4.0Sep 2023View details →

zenodo40/100

Degradation by brown rot fungi increases the hygroscopicity of heat-treated wood

This dataset contains measurement data from the following publication: Belt, T.; Altgen, M.; Awais, M.; Nopens, M.; Rautkari, L. (2023) Degradation by brown rot fungi increases the hygroscopicity of heat-treated wood. International Biodeterioration & Biodegradation, 186:105690. DOI:<a href="https://doi.org/10.1016/j.ibiod.2023.105690">10.1016/j.ibiod.2023.105690</a>. Scots pine sapwood samples were heat-treated under superheated steam and then exposed to brown rot decay by Coniophora puteana and Rhodonia placenta in a stacked-sample decay test, after which a subset of samples was selected for sorption isotherm measurements and near infrared (NIR) imaging. Further details on the experimental procedures can be found in the publication.  The "Sample IDs and mass data.csv" -file contains the sample IDs and all measured mass data for every sample. Masses m0, m1, m2, and m3 are the masses of the samples in the dry state before modification, in the dry state after modification and leaching, in the wet state at the end of the decay test, and in the dry state after the decay test, respectively. The "Sorption measurements_C puteana.xlsx" and "Sorption measurements_R placenta.xlsx" files contain the sorption measurement data collected from samples degraded by C. puteana and R. placenta, respectively. Each tab in the files shows the mass data recorded for a given sample over the sorption measurement sequence.  The "NIR spectra.csv" -file contains the unprocessed average NIR spectra extracted from the NIR images. Details on the extraction of average spectra from the images can be found in the publication.

opencc-by-4.0May 2023View details →

zenodo40/100

Supplementary material for Dynamics of rare earth elements and associated major and trace elements during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation

Database and figures related to the results that are presented in the manusciript Montemagno et al. that will be submitted as research paper in the journal Copernicus/Biogeosciences. Title of the submission: Dynamics of Rare Earth Elements and associated major cations during Douglas-fir (Pseudotsuga menziesii) and European beech (Fagus sylvatica L.) litter degradation. Authorship:  Alessandro Montemagno1,3, Christophe Hissler1, Johanna Ziebel2, Victor Bense3, Laurent Pfister1, Adriaan J. Teuling3 1CATchment and ecohydrology research group (CAT/ENVISION/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg 2Biotechnologies and Environmental Analytics Platform (BEAP/ERIN), Luxembourg Institute of Science and Technology, Belvaux, 4408, Luxembourg 3Department of Environmental Sciences, subdivision Hydrology and Quantitative Water Management (HWQM), Wageningen University and Research, Droevendaalsesteeg 4, Wageningen, 6708 PB, The Netherlands Correspondence: Alessandro Montemagno (<a href="mailto:alessandro.montemagno@list.lu">alessandro.montemagno@list.lu</a>), Christophe Hissler (<a href="mailto:christophe.hissler@list.lu">christophe.hissler@list.lu</a>)

opencc-by-4.0Oct 2021View details →

dryad40/100