cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/8rp-ptn-ls2
title: Pseudobiarritzina proteiformis (Goës, 1882)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cq0-ywb-9xn
title: †Cymbalopora radiata Hagenow, 1851

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6kp-6r3-vtj
title: Pseudorotalia Reiss & Merling, 1958

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/akw-fqy-q9s
title: Pseudopus

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9wt-274-rlp
title: Hoeglundina elegans (d'Orbigny, 1826)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a20-lwg-n8m
title: Cibicides refulgens Montfort, 1808

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/91n-bxa-flg
title: Pyrulina gutta (d'Orbigny, 1826)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3eg-mqy-bpl
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lbq-97n-3mq
title: Amphistegina lessonii d'Orbigny in Deshayes, 1830

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/72r-d1c-kwg
title: †Rhynchonella Fischer de Waldheim, 1809

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/r8p-efq-fsl
title: Sorites orbiculus (Forsskål in Niebuhr, 1775)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xa6-azk-1xl
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ylt-5y1-8j0
title: †Orbitoides sanctaepelagiae (Astre, 1928)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rn0-9at-sy8
title: Cibicidoides variabilis (d'Orbigny, 1826)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/k2f-4cz-eza
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/sr6-m4n-pp5
title: Ursus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/07v-6xv-p06
title: Latonia Meyer, 1843

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3sd-qxy-zje
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/hnb-lxf-k7a
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/l2l-1mg-q5m
title: Tritaxilina caperata (Brady, 1881)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qma-xfg-5wr
title: †Guttulina adhaerens (Olszewski, 1875)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fev-zxf-6wm
title: Pyrgo comata (Brady, 1881)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ra4-hs0-m2x
title: Gavelinopsis Hofker, 1951

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.xh65y3
title: Occurrence Download

description: A dataset containing 10346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aesculus hippocastanum",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10346 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022096-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/63j-mae-08l
title: Lenticulina iota (Cushman, 1923)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lf7-52v-f2a
title: †Paralabamina toulmini (Brotzen, 1948)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/6al-pdz-nqp
title: Alveolophragmium Shchedrina, 1936

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/nwc-geh-ckd
title: Calcarina quoyi d'Orbigny in Fornasini, 1908

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lj2-gwr-pfb
title: †Pararotalia tuberculifera (Reuss, 1862)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/466-zy6-n4g
title: †Globorotalia menardii (d'Orbigny in Parker, Jones & Brady, 1865)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/k6q-4xt-aer
title: †Cancris subconicus (Terquem, 1882)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c1r-xaz-z68
title: Triloculina chemnitziana d'Orbigny, 1839

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j1m-09b-ds4
title: Trifarina Cushman, 1923

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.q99ebq
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aesculus hippocastanum",
 "Country is Portugal",
 "HasCoordinate is true"
 ]
}

 The dataset includes 46 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022097-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/318-9sd-97l
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/44d-f6y-glv
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.13130/rd_unimi/qa4bro/ebrr9f
title: 250408_NONOPT1_TensileTest_109.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_109.tif
DOI: 10.13130/rd_unimi/qa4bro/wn4wh0
title: 250408_NONOPT1_TensileTest_110.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_110.tif
DOI: 10.13130/rd_unimi/qa4bro/xaotzj
title: 250408_NONOPT1_TensileTest_111.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_111.tif
DOI: 10.13130/rd_unimi/qa4bro/zwvxqq
title: 250408_NONOPT1_TensileTest_112.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_112.tif
DOI: 10.13130/rd_unimi/qa4bro/pjddmg
title: 250408_NONOPT1_TensileTest_113.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_113.tif
DOI: 10.13130/rd_unimi/qa4bro/y8nsxr
title: 250408_NONOPT1_TensileTest_114.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_114.tif
DOI: 10.13130/rd_unimi/qa4bro/eirvxn
title: 250408_NONOPT1_TensileTest_115.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_115.tif
DOI: 10.13130/rd_unimi/qa4bro/e2gsx6
title: 250408_NONOPT1_TensileTest_116.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_116.tif
DOI: 10.13130/rd_unimi/qa4bro/fascdf
title: 250408_NONOPT1_TensileTest_117.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_117.tif
DOI: 10.13130/rd_unimi/qa4bro/kqexef
title: 250408_NONOPT1_TensileTest_118.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_118.tif
DOI: 10.13130/rd_unimi/qa4bro/vtlfwv
title: 250408_NONOPT1_TensileTest_119.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_119.tif
DOI: 10.48436/14n5z-axt67
title: Dataset for Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation

description: Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation

This dataset is associated with the Master's Thesis "Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation" and includes all relevant datasets that were created to facilitate experiments conducted. The thesis included the evaluation of SR algorithms on the downstream task of building delineation on the example of Austria. To achieve this, several datasets had to be accessed and created, which are featured in this repository. Further information regarding the process involved, code repositories, and the published thesis are accessible under the GitHub repository: https://github.com/Zerhigh/Evaluating_Sentinel-2_Super-Resolution_Algorithms_for_Automated_Building_Delineation

Structure & Processing Details

All image files are processed similarly:  



Remote sensing images are saved as geotiffs with provided spatial transformation parameters. When using these images, retain their spatial attributes. 

Images are processed and annotated with STAC metadata, with each folder containing its own collection.


The following datasets are available:



main datasets:



hr_masks: 2.5m resolution cadastral masks with building footprints

hr_orthophoto: 2.5m resolution orthophotos of Austria

lr_s2: 10m resolution Sentinel-2 images of Austria (temporally and spatially aligned with the other data sources)



image_samples: samples dataset representing the structure of this data repository

building_delineation_inference: building delineation masks extracted from super-resolved or interpolated Sentinel-2 and orthophoto images

metric_results: results from the conducted experiments on presented metrics

stratification_tables: train/validation/test splits for different dataset configurations

super_resolved: super-resolved Sentinel-2 images (from lr_s2) output from all used SR models

tracasa_evaluation: dataset to achieve evaluation on a small subset for proprietary SR models

thesis_figures: figures and plots featured in the written thesis


This dataset contains only the image data and results, code repositories are available on the linked GitHub repository.
DOI: 10.48436/vzxjz-q2h34
title: Dataset for Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation

description: Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation

This dataset is associated with the Master's Thesis "Evaluating Sentinel-2 Super-Resolution Algorithms for Automated Building Delineation" and includes all relevant datasets that were created to facilitate experiments conducted. The thesis included the evaluation of SR algorithms on the downstream task of building delineation on the example of Austria. To achieve this, several datasets had to be accessed and created, which are featured in this repository. Further information regarding the process involved, code repositories, and the published thesis are accessible under the GitHub repository: https://github.com/Zerhigh/Evaluating_Sentinel-2_Super-Resolution_Algorithms_for_Automated_Building_Delineation

Structure & Processing Details

All image files are processed similarly:  



Remote sensing images are saved as geotiffs with provided spatial transformation parameters. When using these images, retain their spatial attributes. 

Images are processed and annotated with STAC metadata, with each folder containing its own collection.


The following datasets are available:



main datasets:



hr_masks: 2.5m resolution cadastral masks with building footprints

hr_orthophoto: 2.5m resolution orthophotos of Austria

lr_s2: 10m resolution Sentinel-2 images of Austria (temporally and spatially aligned with the other data sources)



image_samples: samples dataset representing the structure of this data repository

building_delineation_inference: building delineation masks extracted from super-resolved or interpolated Sentinel-2 and orthophoto images

metric_results: results from the conducted experiments on presented metrics

stratification_tables: train/validation/test splits for different dataset configurations

super_resolved: super-resolved Sentinel-2 images (from lr_s2) output from all used SR models

tracasa_evaluation: dataset to achieve evaluation on a small subset for proprietary SR models

thesis_figures: figures and plots featured in the written thesis


This dataset contains only the image data and results, code repositories are available on the linked GitHub repository.
DOI: 10.13130/rd_unimi/qa4bro/660lyr
title: 250408_NONOPT1_TensileTest_120.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_120.tif
DOI: 10.13130/rd_unimi/qa4bro/pypcsi
title: 250408_NONOPT1_TensileTest_121.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_121.tif
DOI: 10.13130/rd_unimi/qa4bro/bwsocc
title: 250408_NONOPT1_TensileTest_123.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_123.tif
DOI: 10.13130/rd_unimi/qa4bro/kzarjp
title: 250408_NONOPT1_TensileTest_122.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_122.tif
DOI: 10.13130/rd_unimi/qa4bro/bivoyu
title: 250408_NONOPT1_TensileTest_124.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_124.tif
DOI: 10.13130/rd_unimi/qa4bro/6besyl
title: 250408_NONOPT1_TensileTest_125.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_125.tif
DOI: 10.13130/rd_unimi/qa4bro/ryumgr
title: 250408_NONOPT1_TensileTest_126.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_126.tif
DOI: 10.13130/rd_unimi/qa4bro/2kukxa
title: 250408_NONOPT1_TensileTest_127.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_127.tif
DOI: 10.13130/rd_unimi/qa4bro/utk9yj
title: 250408_NONOPT1_TensileTest_128.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_128.tif
DOI: 10.13130/rd_unimi/qa4bro/7yjl3q
title: 250408_NONOPT1_TensileTest_129.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_129.tif
DOI: 10.13130/rd_unimi/qa4bro/yft9vn
title: 250408_NONOPT1_TensileTest_130.tif

description: 250729_EXPERIMENTAL_DATA/250408_NONOPT1_Video 250408_NONOPT1_TensileTest_130.tif
DOI: 10.6084/m9.figshare.29109800
title: SC-PREC4SA data - full (glm)

description: Files (.csv) for each station that include the nine daily precipitation (mm/day) outputs for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA) from 1960 to 2015. Each file contains the following information (<b>headers</b>): time step (<b>time_step</b>), raw time series (<b>raw_obs</b>), quality-controlled time series (<b>qc_obs</b>), gap-filling model prediction (<b>mod_pred</b>), gap-filling model prediction with a bias correction (<b>bc_pred</b>), error of gap-filling model prediction (<b>err</b>), quality-controlled time series plus mod_pred (<b>obs_mod</b>), quality-controlled time series plus bc_pred (<b>obs_bc</b>), homogenized time series of obs_mod (<b>hmg_obs_mod</b>); and, homogenized time series of obs_bc (<b>hmg_obs_bc</b>). Due to the number of stations, files are subdivided by ecoregions compressed folders (.zip).
DOI: 10.6084/m9.figshare.29109800.v1
title: SC-PREC4SA data - full (glm)

description: Files (.csv) for each station that include the nine daily precipitation (mm/day) outputs for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA) from 1960 to 2015. Each file contains the following information (<b>headers</b>): time step (<b>time_step</b>), raw time series (<b>raw_obs</b>), quality-controlled time series (<b>qc_obs</b>), gap-filling model prediction (<b>mod_pred</b>), gap-filling model prediction with a bias correction (<b>bc_pred</b>), error of gap-filling model prediction (<b>err</b>), quality-controlled time series plus mod_pred (<b>obs_mod</b>), quality-controlled time series plus bc_pred (<b>obs_bc</b>), homogenized time series of obs_mod (<b>hmg_obs_mod</b>); and, homogenized time series of obs_bc (<b>hmg_obs_bc</b>). Due to the number of stations, files are subdivided by ecoregions compressed folders (.zip).
DOI: 10.6084/m9.figshare.29109812
title: SC-PREC4SA gap-filling metrics (rf)

description: A file (.csv) provides information about each station's gap-filling evaluation metrics for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA). The file contains the following information (<b>headers</b>): station code (<b>ID</b>), station ecoregion (<b>ECOREGIONS</b>), type of gap-filling model (mod_pred or bc_pred; <b>MOD</b>), pairwise number of dates used to calculate the metrics (<b>n_data</b>), refined index of agreement (<b>dr</b>), mean absolute error (<b>mae</b>), root mean squared error (<b>rmse</b>), normalized mean absolute error (<b>nmae</b>), normalized root mean squared error (<b>nrmse</b>), accuracy (<b>accuracy</b>), precision (<b>precision</b>), recall (<b>recall</b>), F-measure (<b>f1</b>), balanced accuracy (<b>bcc</b>), G-mean (<b>g_mean</b>). In addition, the percentage of wet days was added (<b>wet_day</b>).
DOI: 10.6084/m9.figshare.29109812.v1
title: SC-PREC4SA gap-filling metrics (rf)

description: A file (.csv) provides information about each station's gap-filling evaluation metrics for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA). The file contains the following information (<b>headers</b>): station code (<b>ID</b>), station ecoregion (<b>ECOREGIONS</b>), type of gap-filling model (mod_pred or bc_pred; <b>MOD</b>), pairwise number of dates used to calculate the metrics (<b>n_data</b>), refined index of agreement (<b>dr</b>), mean absolute error (<b>mae</b>), root mean squared error (<b>rmse</b>), normalized mean absolute error (<b>nmae</b>), normalized root mean squared error (<b>nrmse</b>), accuracy (<b>accuracy</b>), precision (<b>precision</b>), recall (<b>recall</b>), F-measure (<b>f1</b>), balanced accuracy (<b>bcc</b>), G-mean (<b>g_mean</b>). In addition, the percentage of wet days was added (<b>wet_day</b>).
DOI: 10.6084/m9.figshare.29109824
title: SC-PREC4SA data (rf)

description: Files (.csv) for each station that include the nine daily precipitation (mm/day) outputs for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA) from 1960 to 2015. Each file contains the following information (<b>headers</b>): time step (<b>time_step</b>), raw time series (<b>raw_obs</b>), quality-controlled time series (<b>qc_obs</b>), gap-filling model prediction (<b>mod_pred</b>), gap-filling model prediction with a bias correction (<b>bc_pred</b>), error of gap-filling model prediction (<b>err</b>), quality-controlled time series plus mod_pred (<b>obs_mod</b>), quality-controlled time series plus bc_pred (<b>obs_bc</b>), homogenized time series of obs_mod (<b>hmg_obs_mod</b>); and, homogenized time series of obs_bc (<b>hmg_obs_bc</b>). Due to the number of stations, files are subdivided by ecoregions compressed folders (.zip).
DOI: 10.6084/m9.figshare.29109785
title: SC-PREC4SA data (glm)

description: Files (.csv) for each station that include the nine daily precipitation (mm/day) outputs for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA) from 1960 to 2015. Each file contains the following information (<b>headers</b>): time step (<b>time_step</b>), raw time series (<b>raw_obs</b>), quality-controlled time series (<b>qc_obs</b>), gap-filling model prediction (<b>mod_pred</b>), gap-filling model prediction with a bias correction (<b>bc_pred</b>), error of gap-filling model prediction (<b>err</b>), quality-controlled time series plus mod_pred (<b>obs_mod</b>), quality-controlled time series plus bc_pred (<b>obs_bc</b>), homogenized time series of obs_mod (<b>hmg_obs_mod</b>); and, homogenized time series of obs_bc (<b>hmg_obs_bc</b>). Due to the number of stations, files are subdivided by ecoregions compressed folders (.zip).
DOI: 10.6084/m9.figshare.29109827
title: SC-PREC4SA data - full (rf)

description: Files (.csv) for each station that include the nine daily precipitation (mm/day) outputs for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA) from 1960 to 2015. Each file contains the following information (<b>headers</b>): time step (<b>time_step</b>), raw time series (<b>raw_obs</b>), quality-controlled time series (<b>qc_obs</b>), gap-filling model prediction (<b>mod_pred</b>), gap-filling model prediction with a bias correction (<b>bc_pred</b>), error of gap-filling model prediction (<b>err</b>), quality-controlled time series plus mod_pred (<b>obs_mod</b>), quality-controlled time series plus bc_pred (<b>obs_bc</b>), homogenized time series of obs_mod (<b>hmg_obs_mod</b>); and, homogenized time series of obs_bc (<b>hmg_obs_bc</b>). Due to the number of stations, files are subdivided by ecoregions compressed folders (.zip).
DOI: 10.6084/m9.figshare.29109761
title: SC-PREC4SA gap-filling metrics (glm)

description: A file (.csv) provides information about each station's gap-filling evaluation metrics for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA). The file contains the following information (<b>headers</b>): station code (<b>ID</b>), station ecoregion (<b>ECOREGIONS</b>), type of gap-filling model (mod_pred or bc_pred; <b>MOD</b>), pairwise number of dates used to calculate the metrics (<b>n_data</b>), refined index of agreement (<b>dr</b>), mean absolute error (<b>mae</b>), root mean squared error (<b>rmse</b>), normalized mean absolute error (<b>nmae</b>), normalized root mean squared error (<b>nrmse</b>), accuracy (<b>accuracy</b>), precision (<b>precision</b>), recall (<b>recall</b>), F-measure (<b>f1</b>), balanced accuracy (<b>bcc</b>), G-mean (<b>g_mean</b>). In addition, the percentage of wet days was added (<b>wet_day</b>).
DOI: 10.6084/m9.figshare.29109761.v1
title: SC-PREC4SA gap-filling metrics (glm)

description: A file (.csv) provides information about each station's gap-filling evaluation metrics for the development of a serially complete daily precipitation dataset for South America (SC-PREC4SA). The file contains the following information (<b>headers</b>): station code (<b>ID</b>), station ecoregion (<b>ECOREGIONS</b>), type of gap-filling model (mod_pred or bc_pred; <b>MOD</b>), pairwise number of dates used to calculate the metrics (<b>n_data</b>), refined index of agreement (<b>dr</b>), mean absolute error (<b>mae</b>), root mean squared error (<b>rmse</b>), normalized mean absolute error (<b>nmae</b>), normalized root mean squared error (<b>nrmse</b>), accuracy (<b>accuracy</b>), precision (<b>precision</b>), recall (<b>recall</b>), F-measure (<b>f1</b>), balanced accuracy (<b>bcc</b>), G-mean (<b>g_mean</b>). In addition, the percentage of wet days was added (<b>wet_day</b>).