cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10884340
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-09-01/2000-10-31): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-09-01/2000-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884341
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-09-01/2000-10-31): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-09-01/2000-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884311
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-07-01/2000-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884312
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-07-01/2000-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884296
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-05-01/2000-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884297
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-05-01/2000-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884234
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-01-01/2000-02-28): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-01-01/2000-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.5281/zenodo.10884235
title: Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-01-01/2000-02-28): Spectral indices

description: Data information
        
This dataset includes widely used indices for 2000-01-01/2000-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).
        

            
Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as (nir - red) / (nir + red) (Tucker, 1979).
            
Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as (swir1 - swir2) / (swir1 + swir2) (Van Deventer et al., 1997).
            
Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as ((nir - red) / (nir + red + 0.5)) * 1.5 (Huete, 1988).
            
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as ((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001 (Robinson et al., 2018).
            
Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as (nir - swir1) / (nir + swir1) (Gao, 1996).
            
Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as (green - swir1) / (green + swir1) (Salomonson and Appel, 2006).
        
        
As a part of a Data Cube
        
This data represents a subset of the Time-series of Landsat-based Spectral Indices (EU, 30m) data cube. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.
        

            
To cite this dataset, refer to the DOI available on the landing page.
            
To access other data layers in the data cube, use the navigation catalog on the landing page as well.
        
        
Support
        
If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a Github Issue!
DOI: 10.15468/dl.d2zans
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Parapolystichum effusum var. divergens (Willd. ex Schkuhr) Abbiatti)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035632-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24599616
title: Using synchronized brain rhythms to bias memory guided-decisions - eLife

description: Hello! :)These are the source data, which includes LFPs and behavioral data, for the eLife paper titled "Using synchronized brain rhythms to bias memory-guided decisions".These data are accompanied by code to reproduce all figures and statistics in the paper (GriffinLabCode/Manuscript Code/Published/Stout et al 2023 - Using synchronized brain rhythms to bias memory guided decisions/SCRIPT_sourceFigs.m at main · JohnStout/GriffinLabCode · GitHub)Please note that you will likely need the Griffin Lab pipeline to make use of some helper functions (GitHub - JohnStout/GriffinLabCode: Code used for processing and analyzing data from Amy Griffins laboratory at the University of Delaware)The instructions for using this pipeline are in the "1) READ ME.txt" file.It is important to note that some of the data used for this paper are from Hallock et al., 2016 (Ventral Midline Thalamus Is Critical for Hippocampal–Prefrontal Synchrony and Spatial Working Memory | Journal of Neuroscience (jneurosci.org)) and Stout and Griffin 2020 (Frontiers | Representations of On-Going Behavior and Future Actions During a Spatial Working Memory Task by a High Firing-Rate Population of Medial Prefrontal Cortex Neurons (frontiersin.org)).Data for Figure 6 - Figure Supplement 1 can be found here: https://doi.org/10.6084/m9.figshare.26337268.v1Please note that if you are downloading any of the "dataSpkLFP" variables and you explore the dataSpkLFP.session1.processed.info, you will see old information about indexing. Please note, to extract PFC signals, run the following: dataSpkLFP.session1.processed.spksLFPs{1,1}(1,:); To extract HPC signals: dataSpkLFP.session1.processed.spksLFPs{1,1}(2,:); and to extract VMT signals: dataSpkLFP.session1.processed.spksLFPs{1,1}(3,:);If you load the separate dataSpkLFP variables and go to the Github code, jump to lines 1191:1225 to generate a single dataSpkLFP variable.Contact: John Stout, john.j.stout.jr@gmail.com
DOI: 10.6084/m9.figshare.24599616.v6
title: Using synchronized brain rhythms to bias memory guided-decisions - eLife

description: Hello! :)These are the source data, which includes LFPs and behavioral data, for the eLife paper titled "Using synchronized brain rhythms to bias memory-guided decisions".These data are accompanied by code to reproduce all figures and statistics in the paper (GriffinLabCode/Manuscript Code/Published/Stout et al 2023 - Using synchronized brain rhythms to bias memory guided decisions/SCRIPT_sourceFigs.m at main · JohnStout/GriffinLabCode · GitHub)Please note that you will likely need the Griffin Lab pipeline to make use of some helper functions (GitHub - JohnStout/GriffinLabCode: Code used for processing and analyzing data from Amy Griffins laboratory at the University of Delaware)The instructions for using this pipeline are in the "1) READ ME.txt" file.It is important to note that some of the data used for this paper are from Hallock et al., 2016 (Ventral Midline Thalamus Is Critical for Hippocampal–Prefrontal Synchrony and Spatial Working Memory | Journal of Neuroscience (jneurosci.org)) and Stout and Griffin 2020 (Frontiers | Representations of On-Going Behavior and Future Actions During a Spatial Working Memory Task by a High Firing-Rate Population of Medial Prefrontal Cortex Neurons (frontiersin.org)).Data for Figure 6 - Figure Supplement 1 can be found here: https://doi.org/10.6084/m9.figshare.26337268.v1Please note that if you are downloading any of the "dataSpkLFP" variables and you explore the dataSpkLFP.session1.processed.info, you will see old information about indexing. Please note, to extract PFC signals, run the following: dataSpkLFP.session1.processed.spksLFPs{1,1}(1,:); To extract HPC signals: dataSpkLFP.session1.processed.spksLFPs{1,1}(2,:); and to extract VMT signals: dataSpkLFP.session1.processed.spksLFPs{1,1}(3,:);If you load the separate dataSpkLFP variables and go to the Github code, jump to lines 1191:1225 to generate a single dataSpkLFP variable.Contact: John Stout, john.j.stout.jr@gmail.com