cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6868945
title: Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)

description: The dataset is the supplementary material for the following journal paper: Parisse Barbara*, Alilla Roberta, Pepe Antonio Gerardo, De Natale Flora <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em> (Submitted to Elsevier Data in Brief) <strong>Abstract</strong> The <strong>MADIA gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, across the Italian domain for the period <strong>1981-2021</strong>, and their respective 1981-2010 and 1991-2020 <strong>climate normals</strong>. Temporal and spatial resolutions are <strong>10-daily</strong> and <strong>0.25 degrees</strong> respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both <strong>NetCDF </strong>and <strong>csv </strong>format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian <strong>NUTS 2 and 3</strong> levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. The MADIA dataset will be periodically updated. <strong>Attached content</strong> A ZIP archive composed by the following folders nc_data: annual time series from 1981 to 2021 and climate normals (1981-2010 and 1991-2020) in NetCDF format csv_data: annual time series from 1981 to 2021 and climate normals (1981-2010 and 1991-2020) in csv format metadata: discovery and description metadata shp_data: complementary vector data with the NUTS2-3 cover fractions <strong>Acknowledgments</strong> This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)
description: {"references": ["B. Parisse, A. Pontrandolfi, C. Epifani, R. Alilla, F. De Natale (2020) An agrometeorological analysis of weather extremes supporting decisions for the agricultural policies in Italy. Italian Journal of Agrometeorology (3): 15-30. https://doi.org/10.13128/ijam-790"]}
DOI: 10.5281/zenodo.7252361
title: Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)

description: The dataset is the supplementary material for the following journal paper: Parisse B.*, Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area,</em> Data in Brief, 46 (2023), 108843, 10.1016/j.dib.2022.108843, (https://www.sciencedirect.com/science/article/pii/S2352340922010460) <strong>Abstract</strong> The <strong>MADIA gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, across the Italian domain for the period <strong>1981-2021</strong>, and their respective 1981-2010 and 1991-2020 <strong>climate normals</strong>,<strong> </strong>as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5<sup>th</sup>, 10<sup>th</sup>, 50<sup>th</sup>, 90<sup>th</sup>, 95<sup>th</sup> percentiles. Temporal and spatial resolutions are <strong>10-daily</strong> and <strong>0.25 degrees</strong> respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both <strong>NetCDF </strong>and <strong>csv </strong>format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian <strong>NUTS 2 and 3</strong> levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. The MADIA dataset will be periodically updated. <strong>Attached content</strong> A ZIP archive composed by the following folders nc_data: annual time series from 1981 to 2021 and climate normals (1981-2010 and 1991-2020) in NetCDF format csv_data: annual time series from 1981 to 2021 and climate normals (1981-2010 and 1991-2020) in csv format metadata: discovery and description metadata shp_data: complementary vector data with the NUTS2-3 cover fractions <strong>Acknowledgments</strong> This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)
description: In this version (v.1.1) the main statistic on the 30-year dekadal values of each variable (absolute minimum and maximum, 5th, 10th, 50th, 90th, 95th percentiles) have been integrated into the climate normals.
description: {"references": ["B. Parisse, A. Pontrandolfi, C. Epifani, R. Alilla, F. De Natale (2020) An agrometeorological analysis of weather extremes supporting decisions for the agricultural policies in Italy. Italian Journal of Agrometeorology (3): 15-30. https://doi.org/10.13128/ijam-790"]}
DOI: 10.5281/zenodo.7760402
title: Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)

description:  


The dataset is the supplementary material for the following journal paper:


Parisse B.*, Alilla R., Pepe A.G., De Natale F., MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area, Data in Brief, 46 (2023), 108843, 10.1016/j.dib.2022.108843, (https://www.sciencedirect.com/science/article/pii/S2352340922010460)






 


Abstract


The MADIA gridded dataset provides the series of the main agro-meteorological variables derived from ERA5 hourly surface data, across the Italian domain for the period 1981-2022, and their respective 1981-2010 and 1991-2020 climate normals, as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5th, 10th, 50th, 90th, 95th percentiles. Temporal and spatial resolutions are 10-daily and 0.25 degrees respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both NetCDF and csv format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the ERA5 cell polygons covering the Italian country for visualizing and mapping csv data. 


A daily version of the MADIA dataset (only in csv format) is also available on Zenodo at https://doi.org/10.5281/zenodo.7621453.


Both MADIA datasets will be periodically updated.


Attached content


A ZIP archive composed by the following folders



	
nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format
	
csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format
	
metadata: discovery and description metadata 
	
shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy



Acknowledgments


This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)
description: In this version (v.1.2) the MADIA dataset has been updated to 2022 both in NetCDF and csv format. Moreover an additional shapefile ("ERA5_cell_polygons_4_Italy") has been provided to visualize and map the MADIA csv dataset.
DOI: 10.5281/zenodo.4678764
title: Principles of Jurisprudence [IO Islamic 594] الحسامي

description: Principles of Jurisprudence.
	
This manuscript is now IO Islamic 594 in the India Office collections.
	
[metadata: Otto Loth, A Catalogue of the Arabic Manuscripts in the Library of the India Office, (volume 1), no. 294 here with further notations and hyperlinks].



294.


594. Size 11 in. by 6 in.; foll. 292. Five lines in a page.


Another copy of the preceding work.


Well written in a large current hand, with many notes, but worm-eaten and injured by damp towards the end. The colophon is nearly destroyed; but the date, A.H. 914 [=1508-9 CE], is still legible. Several leaves are missing after fol. 290, and foll. 1-17 have been supplied by a different hand.


[Johnson.]
DOI: 10.5281/zenodo.4678765
title: Principles of Jurisprudence [IO Islamic 594] الحسامي

description: Principles of Jurisprudence.
	
This manuscript is now IO Islamic 594 in the India Office collections.
	
[metadata: Otto Loth, A Catalogue of the Arabic Manuscripts in the Library of the India Office, (volume 1), no. 294 here with further notations and hyperlinks].



294.


594. Size 11 in. by 6 in.; foll. 292. Five lines in a page.


Another copy of the preceding work.


Well written in a large current hand, with many notes, but worm-eaten and injured by damp towards the end. The colophon is nearly destroyed; but the date, A.H. 914 [=1508-9 CE], is still legible. Several leaves are missing after fol. 290, and foll. 1-17 have been supplied by a different hand.


[Johnson.]
DOI: 10.5281/zenodo.14097216
title: Spatially Disaggregated Energy Consumption and Emissions in End-use Sectors for Germany and Spain

description: The Greenhouse Gas (GHG) emissions and Final Energy Consumption (FEC) across end-use sectors - industry, transport, agriculture, households, and commerce - are spatially disaggregated from the national level to the Local Administrative Unit (LAU) level. The disaggregation is carried out for two countries, Germany and Spain.
DOI: 10.5281/zenodo.14097217
title: Spatially Disaggregated Energy Consumption and Emissions in End-use Sectors for Germany and Spain

description: The Greenhouse Gas (GHG) emissions and Final Energy Consumption (FEC) across end-use sectors - industry, transport, agriculture, households, and commerce - are spatially disaggregated from the national level to the Local Administrative Unit (LAU) level. The disaggregation is carried out for two countries, Germany and Spain.
DOI: 10.15468/dl.rysdjy
title: Occurrence Download

description: A dataset containing 965 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Norwegian freshwater lake fish inventory"
 ]
}

 The dataset includes 965 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005245-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/1ro4uh
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DOI: 10.57745/2s8n9n
title: data_000000.raw
DOI: 10.57745/35fwmu
title: qcmlinux_v250116.iso
DOI: 10.57745/3khhj6
title: QCM_virtual_U2004_U2204
DOI: 10.57745/4tjg3p
title: data_000006.raw
DOI: 10.57745/7esd5g
title: QCM_virtual_U24.desktop
DOI: 10.57745/b83wzo
title: data_000012.raw
DOI: 10.57745/bniy3q
title: data_info.txt
DOI: 10.57745/c7cvcd
title: readme.txt
DOI: 10.57745/cnmu06
title: QCM_Manual_v1_0.pdf
DOI: 10.57745/cxmcx6
title: QCM_virtual_U2404
DOI: 10.57745/ezj3ub
title: data_000009.raw
DOI: 10.57745/ghw7cw
title: readme.txt
DOI: 10.57745/hy4jzp
title: Licence.md
DOI: 10.57745/ivngkw
title: SR_MAP_for_80pc_of_particules_18_42dB_D0p05_5_r0_connu_m50_gain0p5_poisson_pixel_107nm_r0_1p25_400.pgm
DOI: 10.57745/j2tfhz
title: QCM_virtual_U2404
DOI: 10.57745/jwsqzv
title: data_000001.raw
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DOI: 10.57745/lm7gmx
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DOI: 10.57745/lzx6ar
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DOI: 10.57745/ni2qry
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title: data_000000.raw
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DOI: 10.57745/pjgqws
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DOI: 10.57745/pu9yul
title: data_info.txt
DOI: 10.57745/r6wige
title: data_000010.raw
DOI: 10.57745/ryuzzv
title: QCM_virtual_U20.desktop
DOI: 10.57745/tonill
title: readme_program.md
DOI: 10.57745/wwdb6x
title: qcm.svg
DOI: 10.57745/xr9eta
title: data_000005.raw
DOI: 10.57745/ztoovf
title: data_000014.raw
DOI: 10.57745/0cptmd
title: density_win_qcm.png
DOI: 10.57745/14xmbm
title: histogram.png
DOI: 10.57745/1cafkk
title: data_info.txt
DOI: 10.57745/1ei5bm
title: data_info.txt
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DOI: 10.57745/2wlow5
title: density.png
DOI: 10.57745/309zn4
title: data_info.txt
DOI: 10.57745/3ncydg
title: data_time_expo_frame.raw
DOI: 10.57745/4zlpw9
title: info.txt
DOI: 10.57745/5lakok
title: data_000009.raw
DOI: 10.57745/7q5cuo
title: meanbg.png
DOI: 10.57745/88aubp
title: data_000003.raw
DOI: 10.57745/a0g1lt
title: data_info.txt
DOI: 10.57745/bjytx6
title: data_000007.raw
DOI: 10.57745/bkgywn
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DOI: 10.57745/bqmdqg
title: data_000001.raw
DOI: 10.57745/bxhrf2
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DOI: 10.57745/dy2gjg
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title: density_win_qcm.png
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title: data_000000.raw
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DOI: 10.57745/n5o9p6
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DOI: 10.57745/bzqv9b
title: precision.png
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title: histogram2.png
DOI: 10.57745/cd54qn
title: smlm.png
DOI: 10.57745/cimbrg
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DOI: 10.57745/dgbha6
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DOI: 10.57745/dhn7qa
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DOI: 10.57745/dwusah
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DOI: 10.57745/hkhn7x
title: camera.png
DOI: 10.57745/iglshh
title: precision.png
DOI: 10.57745/imcrgg
title: smlm.png
DOI: 10.57745/jclmey
title: density.png
DOI: 10.57745/kfqhzj
title: camera_win_qcm.png
DOI: 10.57745/klwqmn
title: density.png
DOI: 10.57745/kmp7ao
title: snr.png
DOI: 10.57745/knqhqz
title: precision.png
DOI: 10.57745/mckfgn
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DOI: 10.57745/mhclye
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DOI: 10.57745/ystwfc
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DOI: 10.57745/htcdou
title: density_win_qcm.png
DOI: 10.57745/il9nvp
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DOI: 10.57745/iqwqqc
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DOI: 10.57745/j5jvjx
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DOI: 10.57745/srvik2
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DOI: 10.57745/vvkxlt
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DOI: 10.57745/ygsaah
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DOI: 10.57745/2snpcq
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DOI: 10.57745/4bs2rx
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DOI: 10.57745/4ijkxj
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DOI: 10.57745/6x2riu
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DOI: 10.57745/7yxqub
title: density_win_qcm.png
DOI: 10.57745/8b1061
title: smlm.png
DOI: 10.57745/8kl8mx
title: camera.png
DOI: 10.57745/9jxluj
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