cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7462441
title: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia

description: Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89 000 ha), Poland (Milicz forest district - 21 000 ha) and Slovenia (Snežnik forest - 4700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100 000 ha including about 64 000 ha of forest and consist of more than 42 million trees of 51 different species. For each virtual landscape we provide a table (in .csv format) with the following columns:<br> - cellID25: the unique ID of each 25x25 m² cell<br> - sp: species latin names<br> - n: number of trees<br> - dbh: tree diameter at breast height (cm)<br> - h: tree height (m) We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. Finally, we provide a proof of how multiplying the trees dbh by the α correction coefficient makes it possible to reach the cells BA value derived from the ALS mapping (see algorithm presented in the associated Open Research Europe article). Below is an example of R code that opens the datasets and creates a tree density map. ------------------------------------------------------------<br> # load package<br> library(raster)<br> library(dplyr) # set work directory<br> setwd() # define path to the I-MAESTRO_data folder # load tree data<br> tree &lt;- read.csv2('./milicz/trees.csv', sep = ',') # load spatial data<br> cellID &lt;- raster('./milicz/cellID25.asc') # convert raster into dataframe<br> cellIDdf &lt;- as.data.frame(cellID) # calculate tree density from tree dataframe<br> dens &lt;- tree %&gt;% group_by(cellID25) %&gt;% summarise(n = sum(n)) # merge the two dataframes<br> dens &lt;- left_join(cellIDdf, dens) # add density to raster<br> cellID$dens &lt;- dens$n # plot density map<br> plot(cellID$dens)
description: This work was carried out within the framework of the I-Maestro project, supported under the umbrella of ERA-NET Cofund ForestValue by ADEME (FR), FNR (DE), MIZS (SI), NCN (PL). ForestValue has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n°773324. This work was also supported by the GRAINE program of ADEME (FR) in the framework of the PROTEST project (convention n°1703C0069).
DOI: 10.5281/zenodo.7464873
title: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia

description: Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89 000 ha), Poland (Milicz forest district - 21 000 ha) and Slovenia (Snežnik forest - 4700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100 000 ha including about 64 000 ha of forest and consist of more than 42 million trees of 51 different species. For each virtual landscape we provide a table (in .csv format) with the following columns:<br> - cellID25: the unique ID of each 25x25 m² cell<br> - sp: species latin names<br> - n: number of trees<br> - dbh: tree diameter at breast height (cm)<br> - h: tree height (m) We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. In v1.0.1, useless system files were removed. Finally, we provide a proof of how multiplying the trees dbh by the α correction coefficient makes it possible to reach the cells BA value derived from the ALS mapping (see algorithm presented in the associated Open Research Europe article). Below is an example of R code that opens the datasets and creates a tree density map. ------------------------------------------------------------<br> # load package<br> library(raster)<br> library(dplyr) # set work directory<br> setwd() # define path to the I-MAESTRO_data folder # load tree data<br> tree &lt;- read.csv2('./milicz/trees.csv', sep = ',') # load spatial data<br> cellID &lt;- raster('./milicz/cellID25.asc') # convert raster into dataframe<br> cellIDdf &lt;- as.data.frame(cellID) # calculate tree density from tree dataframe<br> dens &lt;- tree %&gt;% group_by(cellID25) %&gt;% summarise(n = sum(n)) # merge the two dataframes<br> dens &lt;- left_join(cellIDdf, dens) # add density to raster<br> cellID$dens &lt;- dens$n # plot density map<br> plot(cellID$dens)
description: This work was carried out within the framework of the I-Maestro project, supported under the umbrella of ERA-NET Cofund ForestValue by ADEME (FR), FNR (DE), MIZS (SI), NCN (PL). ForestValue has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n°773324. This work was also supported by the GRAINE program of ADEME (FR) in the framework of the PROTEST project (convention n°1703C0069).
DOI: 10.5281/zenodo.7577710
title: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia

description: Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species. For each virtual landscape we provide a table (in .csv format) with the following columns:<br> - cellID25: the unique ID of each 25x25 m² cell<br> - sp: species latin names<br> - n: number of trees<br> - dbh: tree diameter at breast height (cm)<br> - h: tree height (m) We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The v2.0.0 presents the algorithm in its final state. Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article). Below is an example of R code that opens the datasets and creates a tree density map. ------------------------------------------------------------<br> # load package<br> library(raster)<br> library(dplyr) # set work directory<br> setwd() # define path to the I-MAESTRO_data folder # load tree data<br> tree &lt;- read.csv2('./milicz/trees.csv', sep = ',') # load spatial data<br> cellID &lt;- raster('./milicz/cellID25.asc') # convert raster into dataframe<br> cellIDdf &lt;- as.data.frame(cellID) # calculate tree density from tree dataframe<br> dens &lt;- tree %&gt;% group_by(cellID25) %&gt;% summarise(n = sum(n)) # merge the two dataframes<br> dens &lt;- left_join(cellIDdf, dens) # add density to raster<br> cellID$dens &lt;- dens$n # plot density map<br> plot(cellID$dens)
description: This work was carried out within the framework of the I-Maestro project, supported under the umbrella of ERA-NET Cofund ForestValue by ADEME (FR), FNR (DE), MIZS (SI), NCN (PL). ForestValue has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n°773324. This work was also supported by the GRAINE program of ADEME (FR) in the framework of the PROTEST project (convention n°1703C0069).
DOI: 10.5281/zenodo.10204837
title: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia

description: Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species.
For each virtual landscape we provide a table (in .csv format) with the following columns:- cellID25: the unique ID of each 25x25 m² cell- sp: species latin names- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.- dbh: tree diameter at breast height (cm)- h: tree height (m)
We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik.
The v2.0.0 presents the algorithm in its final state.
Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article).
Below is an example of R code that opens the datasets and creates a tree density map.
------------------------------------------------------------# load package
library(terra)
library(dplyr)
 
# set work directory
setwd()              # define path to the I-MAESTRO_data folder
 
# load tree data
tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')
 
# load spatial data
cellID <- rast('./sneznik/sneznik_cellID25.asc')
 
# set coordinate reference system
# Bauges:
# crs(cellID) <- "epsg:2154"
# Milicz:
# crs(cellID) <- "epsg:2180"
# Sneznik:
# crs(cellID) <- "epsg:3912"
 
# convert raster into dataframe
cellIDdf <- as.data.frame(cellID)
colnames(cellIDdf) <- 'cellID25'
 
# calculate tree density from tree dataframe
dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))
 
# merge the two dataframes
dens <- left_join(cellIDdf, dens, join_by(cellID25))
 
# add density to raster
cellID$dens <- dens$n
 
# plot density map
plot(cellID$dens)
description: This work was carried out within the framework of the I-Maestro project, supported under the umbrella of ERA-NET Cofund ForestValue by ADEME (FR), FNR (DE), MIZS (SI), NCN (PL). ForestValue has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n°773324.
This work was also supported by the GRAINE program of ADEME (FR) in the framework of the PROTEST project (convention n°1703C0069).
DOI: 10.5281/zenodo.7462440
title: I-MAESTRO data: 42 million trees from three large European landscapes in France, Poland and Slovenia

description: Here we present three datasets describing three large European landscapes in France (Bauges Geopark - 89,000 ha), Poland (Milicz forest district - 21,000 ha) and Slovenia (Snežnik forest - 4,700 ha) down to the tree level. Individual trees were generated combining inventory plot data, vegetation maps and Airborne Laser Scanning (ALS) data. Together, these landscapes (hereafter virtual landscapes) cover more than 100,000 ha including about 64,000 ha of forest and consist of more than 42 million trees of 51 different species.
For each virtual landscape we provide a table (in .csv format) with the following columns:- cellID25: the unique ID of each 25x25 m² cell- sp: species latin names- n: number of trees. n is an integer >= 1, meaning that a specific set of species "sp", diameter "dbh" and height "h" can be present multiple times in a cell.- dbh: tree diameter at breast height (cm)- h: tree height (m)
We also provide, for each virtual landscape, a raster (in .asc format) with the cell IDs (cellID25) which makes data spatialisation possible. The coordinate reference systems are EPSG: 2154 for the Bauges, EPSG: 2180 for Milicz, and EPSG: 3912 for Sneznik.
The v2.0.0 presents the algorithm in its final state.
Finally, we provide a proof of how our algorithm makes it possible to reach the total BA and the BA proportion of broadleaf trees provided by the ALS mapping using the alpha correction coefficient and how it maintains the Dg ratios observed on the field plots between the different species (see algorithm presented in the associated Open Research Europe article).
Below is an example of R code that opens the datasets and creates a tree density map.
------------------------------------------------------------# load package
library(terra)
library(dplyr)
 
# set work directory
setwd()              # define path to the I-MAESTRO_data folder
 
# load tree data
tree <- read.csv2('./sneznik/sneznik_trees.csv', sep = ',')
 
# load spatial data
cellID <- rast('./sneznik/sneznik_cellID25.asc')
 
# set coordinate reference system
# Bauges:
# crs(cellID) <- "epsg:2154"
# Milicz:
# crs(cellID) <- "epsg:2180"
# Sneznik:
# crs(cellID) <- "epsg:3912"
 
# convert raster into dataframe
cellIDdf <- as.data.frame(cellID)
colnames(cellIDdf) <- 'cellID25'
 
# calculate tree density from tree dataframe
dens <- tree %>% group_by(cellID25) %>% summarise(n = sum(n))
 
# merge the two dataframes
dens <- left_join(cellIDdf, dens, join_by(cellID25))
 
# add density to raster
cellID$dens <- dens$n
 
# plot density map
plot(cellID$dens)
description: This work was carried out within the framework of the I-Maestro project, supported under the umbrella of ERA-NET Cofund ForestValue by ADEME (FR), FNR (DE), MIZS (SI), NCN (PL). ForestValue has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n°773324.
This work was also supported by the GRAINE program of ADEME (FR) in the framework of the PROTEST project (convention n°1703C0069).
DOI: 10.34740/kaggle/ds/4054433
title: Sri Lankan Rupee Rhythms: 13-Year Forex Dance
DOI: 10.5281/zenodo.10204650
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci vjetroelektrana A, B i C

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci uključuju mjerenja radne snage svakog vjetroagregata u vjetroelektrani te korigiranu brzinu vjetra mjerenu na gondoli vjetroagregata za vjetroelektrane A (30 dana), B (11 dana) i C (30 dana). Vremenska rezolucija podataka je 1 s.
DOI: 10.5281/zenodo.10204651
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci vjetroelektrana A, B i C

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci uključuju mjerenja radne snage svakog vjetroagregata u vjetroelektrani te korigiranu brzinu vjetra mjerenu na gondoli vjetroagregata za vjetroelektrane A (30 dana), B (11 dana) i C (30 dana). Vremenska rezolucija podataka je 1 s.
DOI: 10.5281/zenodo.10204475
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci s mjernog stupa

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci su prikupljeni s zapisivača podataka Ammonit Meteo - 40 i uključuju brzinu i smjer vjetra, vlažnost, temperaturu, tlak zraka i ozračenost. 
Meta podaci:
Label Evaluation Origin Value Unit Slope Offset Cardinal Direction Tilt Angle Sensitivity
An1 Wind Speed C1 5.6097 m/s 0.045799 0.251198    
An2 Wind Speed C2 5.2476 m/s 0.045769 0.258771    
An3 Wind Speed C3 4.0996 m/s 0.045746 0.25696    
An4 Wind Speed C4 5.0608 m/s 0.045843 0.247262    
An5 Wind Speed C5 5.5150 m/s 0.045782 0.250029    
Anemometer @3.0m Wind Speed C6 4.9400 m/s 0.765 0.35    
Dir1 Wind Direction D1 34.4219 o 0.3515625 97    
Dir2 Wind Direction D2 57.6797 o 0.3515625 96    
Hygro/Thermo Humidity A3 74.3736 % 100 0    
Hygro/Thermo Temperature A2 13.36680 °C 100 -30    
Barometer Air Pressure A1 965.8254 hPa 60 800    
Horz Global Irradiance A4 42.7259 W/m2     1000
Tilt Global Irradiance A7 0.0000 W/m2     1000
Horz Diffuse Irradiance A5 40.2844 W/m2     1000
Tilt Diffuse Irradiance A8 0.0000 W/m2     1000
Horz Estimated DNI A4, A5 3.8585 W/m2   0 0 1000
Tilt Estimated DNI A7. A8 --- W/m2   0 20 1000
Horz Sun Status D3 1      
Tilt Sun Status D4 1      
Horz Estimated Sun Status A4. A5 0.0000    0 0 1000
Tilt Estimated Sun Status A7. A8 ---    0 20 1000
DOI: 10.5281/zenodo.10204474
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci s mjernog stupa

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci su prikupljeni s zapisivača podataka Ammonit Meteo - 40 i uključuju brzinu i smjer vjetra, vlažnost, temperaturu, tlak zraka i ozračenost. 
Meta podaci:
Label Evaluation Origin Value Unit Slope Offset Cardinal Direction Tilt Angle Sensitivity
An1 Wind Speed C1 5.6097 m/s 0.045799 0.251198    
An2 Wind Speed C2 5.2476 m/s 0.045769 0.258771    
An3 Wind Speed C3 4.0996 m/s 0.045746 0.25696    
An4 Wind Speed C4 5.0608 m/s 0.045843 0.247262    
An5 Wind Speed C5 5.5150 m/s 0.045782 0.250029    
Anemometer @3.0m Wind Speed C6 4.9400 m/s 0.765 0.35    
Dir1 Wind Direction D1 34.4219 o 0.3515625 97    
Dir2 Wind Direction D2 57.6797 o 0.3515625 96    
Hygro/Thermo Humidity A3 74.3736 % 100 0    
Hygro/Thermo Temperature A2 13.36680 °C 100 -30    
Barometer Air Pressure A1 965.8254 hPa 60 800    
Horz Global Irradiance A4 42.7259 W/m2     1000
Tilt Global Irradiance A7 0.0000 W/m2     1000
Horz Diffuse Irradiance A5 40.2844 W/m2     1000
Tilt Diffuse Irradiance A8 0.0000 W/m2     1000
Horz Estimated DNI A4, A5 3.8585 W/m2   0 0 1000
Tilt Estimated DNI A7. A8 --- W/m2   0 20 1000
Horz Sun Status D3 1      
Tilt Sun Status D4 1      
Horz Estimated Sun Status A4. A5 0.0000    0 0 1000
Tilt Estimated Sun Status A7. A8 ---    0 20 1000
DOI: 10.5517/ccdc.csd.cc2btqfb
title: CCDC 2169672: Experimental Crystal Structure Determination

description: Related Article: Xiaoquan Feng|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.10204045
title: IMS/IASI SO2 total column and SA OD monthly means gridded data

description: Datasets in support of the manuscript: https://essopenarchive.org/doi/full/10.22541/essoar.169091894.48592907 (in revision for GRL).
Climatology 2008-2019: MM_ims_metopa_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc
2022: 2022MM_ims_metopb_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc
(MM=month; {so2,aod0}=SO2 total columns or SA OD)
DOI: 10.34740/kaggle/dsv/7046159
title: Información de Precios al Consumidor - uy 2023
DOI: 10.5281/zenodo.10205003
title: Connectome-based reservoir computing with the conn2res toolbox
DOI: 10.5281/zenodo.10205014
title: A mode-locked random laser generating transform-limited optical pulses

description: Ever since the mid-1960's, locking the phases of modes enabled the generation of laser pulses of duration limited only by the uncertainty principle, opening the field of ultrafast science. In contrast to conventional lasers, mode spacing in random lasers is ill-defined because optical feedback comes from scattering centres at random positions, making it hard to use mode locking in transform limited pulse generation. Here the generation of sub-nanosecond transform-limited pulses from a mode-locked random fibre laser is reported. Rayleigh backscattering from decimetre-long sections of telecom fibre serves as laser feedback, providing narrow spectral selectivity to the Fourier limit. The laser is adjustable in pulse duration (0.34-20 ns), repetition rate (0.714-1.22 MHz) and can be temperature tuned. The high spectral-efficiency pulses are applied in distributed temperature sensing with 9.0 cm and 3.3×10⁻³ K resolution, exemplifying how the results can drive advances in the fields of spectroscopy, telecommunications, and sensing.
DOI: 10.5281/zenodo.10205013
title: A mode-locked random laser generating transform-limited optical pulses

description: Ever since the mid-1960's, locking the phases of modes enabled the generation of laser pulses of duration limited only by the uncertainty principle, opening the field of ultrafast science. In contrast to conventional lasers, mode spacing in random lasers is ill-defined because optical feedback comes from scattering centres at random positions, making it hard to use mode locking in transform limited pulse generation. Here the generation of sub-nanosecond transform-limited pulses from a mode-locked random fibre laser is reported. Rayleigh backscattering from decimetre-long sections of telecom fibre serves as laser feedback, providing narrow spectral selectivity to the Fourier limit. The laser is adjustable in pulse duration (0.34-20 ns), repetition rate (0.714-1.22 MHz) and can be temperature tuned. The high spectral-efficiency pulses are applied in distributed temperature sensing with 9.0 cm and 3.3×10⁻³ K resolution, exemplifying how the results can drive advances in the fields of spectroscopy, telecommunications, and sensing.
DOI: 10.22003/xfel.eu-data-900302-00
title: ITLAB SASEd DAQ tests
DOI: 10.22003/xfel.eu-data-900327-00
title: TST SASE2 DAQ tests
DOI: 10.22003/xfel.eu-data-004535-00
title: Metallization and structural transition in solid-dense Krypton
DOI: 10.22003/xfel.eu-data-900334-00
title: ITLAB SASEd DAQ tests
DOI: 10.22003/xfel.eu-data-006616-00
title: Ultrafast pump-probe timing commissioning
DOI: 10.5281/zenodo.10205004
title: Connectome-based reservoir computing with the conn2res toolbox
DOI: 10.5281/zenodo.10205036
title: Residual force depression data

description: Raw data used to create the results text and figures presented in the manuscript entitled "Residual force depression is not related to positive muscle fascicle work during submaximal voluntary dorsiflexion contractions in humans".
DOI: 10.5281/zenodo.10205037
title: Residual force depression data

description: Raw data used to create the results text and figures presented in the manuscript entitled "Residual force depression is not related to positive muscle fascicle work during submaximal voluntary dorsiflexion contractions in humans".
DOI: 10.26093/cds/vizier.36530098
title: asPIC1.1 catalogue

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'The all-sky PLATO input catalog,' (bibcode: 2021A&A...653A..98M)
DOI: 10.5281/zenodo.10204782
title: Weight Scale ALL colors

description: All data pertaining Weight scale measurements
DOI: 10.5281/zenodo.10204783
title: Weight Scale ALL colors

description: All data pertaining Weight scale measurements