Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Warragamba inflow data, predictors and code

This repository contains data and code used for the analyses presented in Devanand et al. (2023) Can climate knowledge enable Warragamba Dam, Sydney, Australia to be used to manage flood risk?, 10.1088/1748-9326/ad0afb. The csv files contain historical inflow into the Warragamba dam in eastern Australia for the period 1970-2022, the large scale climate indices, and antecedent soil moisture. The climate predictors are obtained from https://psl.noaa.gov/gcos_wgsp/Timeseries/. The shape file 'warragamba_catchment.shp' is the catchment area that is used to estimate catchment average soil moisture from the AWRA-L datatset (https://dx.doi.org/10.25914/NDY0-J367). The python notebook 'probability_of_dam_inflows.ipynb' contains the code used to estimate inflow exceedance probabilities as a function of the predictors.

opencc-by-4.0Dec 2021View details →

Dataset - paper: Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors

Dataset corresponding to a paper that has been accepted for publication in Appetite (Philippe, K., Chabanet, C., Issanchou, S., Grønhøj, A., Aschemann-Witzel, J., & Monnery-Patris, S. (2022, in press). Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors. Appetite). The objectives of the study were (1) to examine possible differences between Danish mothers and fathers with regard to their involvement in child feeding and their feeding practices, and (2) to identify possible parent-related predictors of parental feeding practices and of parental involvement in child feeding at home. Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-Denmark.docx".

opencc-by-4.0Apr 2019View details →

RNA datasets to derive predictors for immune checkpoint inhibitor therapy of non-small cell lung cancer

Nanostring nCounter datasets and corresponding clinical data of tumor samples of patients with advanced NSCLC who received anti-PD-1 immuntherapy. Prospectively divided into a discovery and a validation cohort. Please cite the corresponding publication in Annals of Oncology (10.1093/annonc/mdz049)

opencc-by-4.0Oct 2024View details →

Data for predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors

The data was used in the analysis presented in the manuscript: Predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors. The manuscript is published in Animal journal. The data is for piglet survival survival at different time-points from birth to weaning from two research farms.

opencc-by-4.0Apr 2022View details →

Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence

RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year. This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data. The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>). The features in the clinical metadata include: <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual's ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual's sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual's first positive swab was taken at time of sampling</td> </tr> </tbody> </table>

opencc-by-4.0Feb 2023View details →

Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm

CHARACTERISTICS Planet: Venus Radius: RV = 6051.8 km (volumetric mean planetary radius) Spacecraft: ESA/Venus Express Spacecraft coordinates system: Venus Solar Orbital (VSO) equivalent to Sun-State coordinate system: <ul> <li>+XVSO points towards the Sun from the planet’s centre,</li> <li>+ZVSO towards Venus’ North pole and perpendicular to the orbital plane defined as the XVSO–YVSO plane passing through the centre of Venus,</li> <li>YVSO completes the orthogonal system.</li> </ul> Time span: 01/04/2006 to 25/11/2014 Total number N of candidate bow shock crossings in the database: N = 4950 Number of quasi-parallel bow shock crossings: N|| = 844 Number of quasi-perpendicular bow shock crossings: N$\perp$ = 4106 ORIGINAL DATASETS USED The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used. METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a> They consist of two consecutive steps:  <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane $\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)$performed on the datasets of Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted θBn (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that: <ul> <li>45∘<θBn<135∘: quasi-perpendicular shock condition</li> <li>θBn≤45∘ and θBn$\geq$135∘: quasi-parallel shock condition</li> </ul> Uncertainty on these angles is estimated to be ± 5º.  For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. VARIABLES DESCRIPTION This database contains the following ASCII variables: <ul> <li>Bow shock times in Venus Express' database (1-s resolution): Tbs</li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius RV (RV = 6051.8 km): XVSO, YVSO, ZVSO and Euclidean distance $R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}$ (in RV)</li> <li>Solar Zenith angle in degrees: SZA = $\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}$ (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface $\theta_{Bn}$ (ThetaBn, in º, calculated with atan2(norm(cross(B,ñ),dot(B,ñ)), with B the magnetic field vector and ñ the vector normal to the shock surface): <ul> <li>45 < ThetaBn <  135 deg: quasi-$\perp$ shock</li> <li>ThetaBn $\leq$ 45 deg & ThetaBn $\geq$ 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, Bx, By, Bz (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath $\longrightarrow$ solar wind (2447 events)</li> <li>flag = 1: solar wind $\longrightarrow$ magnetosheath (2503 events)</li> </ul> </li> </ul> WARNING <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of: Persson et al. (2023), Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, Zenodo (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 RV (with RV = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> Current formatting optimised for MATLAB. ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36.     LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license. Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF),                                                              Austrian Academy of Sciences, 2022-10-05 Contact email:        cyril.simon.wedlund@gmail.com

edi48/100

Artificial Communities Experiment: Predictor Variables

This study considers the relative role of competition and facilitation in primary succession of the boreal forest floodplain of Bonanza Creek Experimental Forest. Alder (Alnus tenuifolia Nutt.) is an early colonist of young floodplain sites, where it contributes greatly to soil development through the addition of nitrogen to the soil. White spruce (Picea glauca (Moench.) Voss) typically colonizes such sites later; it becomes established beneath a canopy of alder and willow (Salix spp.) which is sometimes overtopped by young balsam poplar (Populus balsamifera L.). The alder may potentially influence white spruce both positively and negatively. Possible facilitative effects include protection of small seedlings from excess heat or heavy siltation accompanying flooding and addition of soil nitrogen, an effect that may extend for many years. Competition for light (and other resources) before the white spruce has overtopped the alder canopy may prove to be a major negative effects. This study seeks to determine the net balance of these effects on the floodplain successional sequence.

openOpenAug 2016View details →

OpenNeuro44/100

Neuroimaging predictors of creativity in healthy adults

Open the record for dataset details and reuse information.

openCC0Jan 2019View details →

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2019): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2019. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2020): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2020. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2015): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2015. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2011): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2011. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2005): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2005. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2000): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2000. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2018): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2018. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2010): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2010. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2004): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2004. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2014): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2014. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2006): BSF, minNDTI, and NOS

<h2>Data information</h2> This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2006. They are calculated as follows: <ul> <li>minNDTI (minimum NDTI) is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li>BSF (bare soil fraction) is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li>NOS (number of seasons) indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!