cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/6pxj-fb19
title: Error-related potentials (Primary and secondary ErrP) and P300 event related potentials – BCI-Double-ErrP-Dataset

description: BCI-Double-ErrP-Dataset is an EEG dataset recorded while participants used a P300-based BCI speller. This speller uses a P300 post-detection based on Error-related potentials (ErrPs) to detect and correct errors (i.e. when the detected symbol does not match the user’s intention). After the P300 detection, an automatic correction is made when an ErrP is detected (this is called a “Primary ErrP”). The correction proposed by the system is also evaluated, eventually eliciting a “Secondary ErrP” if the correction is wrong. The overall approach is called “Double-ErrP detection and correction” [1]. The recorded datasets are useful to research the use of ErrPs to increase BCI reliability and to research how “Secondary ErrPs” can be used to increase the naturalness of Human-machine interaction. The datasets are also useful to research ErrP variability across subjects and across sessions in different days. The EEG signals were recorded from nine able-bodied participants (S1-S9) and one tetraplegic participant (P1) with medullar injury (C4/C5 level) with ages between 24 and 43 years old. However, the data set comprises only 8 subjects because two participants (S7 and S8) took part only in the first session of the experiments. Scalp EEG was recorded at 256 Hz with a g.USBamp bioamplifier (g.tec, Austria).A detailed description of the approach and experiments is given in [1] and a video in [2]. References[1] A. Cruz, G. Pires, U. Nunes (2018), "Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 99, January 2018, doi: 10.1109/TNSRE.2017.2755018. https://ieeexplore.ieee.org/document/8048036 [2] Demonstrative video in http://home.isr.uc.pt/~gpires/ErrP/ Acknowledgement: This data collection was supported in part by Project B-RELIABLE funded by FEDER/FNR/OE through programs CENTRO2020 and Portuguese foundation for science and technology (FCT), under grant SAICT/30935/2017.
DOI: 10.17033/data.00000078
title: A generalised Fully Lagrangian Approach for gas-droplet flows

description: This project synthesises mathematical and engineering approaches to the simulation of gas-droplet flows, and includes the development of a novel mathematical formalism. The project focuses on non-trivial generalisation of the version of the Fully Lagrangian Approach (FLA) developed by Osiptsov, also known as the Osiptsov method. This method is recognised as a promising approach to calculating particle/droplet concentrations. However, its current applications are restricted to specific types of flow within a dilute mono-sized admixture. The novel model to be developed in the framework of the project will take into account droplet evaporation, polydispersity of an admixture, and the effect of droplets on the carrier phase (two-way coupling). The corresponding mathematical model will be formulated and implemented in the Computational Fluid Dynamics (CFD) software OpenFOAM, an open-source and widely used CFD software, which will make the project outcomes accessible to specialists interested in sprays. The developed model will be applied to simulate the evolution of droplet distribution in sprays formed by a gasoline or diesel injector. The results of the numerical simulations will be validated against experimental data collected in the Advanced Engineering Centre (AEC), at the University of Brighton (UoB).
description: This OpenFOAM library is created base on the existing Lagrangian module and PIMPLE solver in OpenFOAM 6.0.
DOI: 10.21227/n407-h102
title: Corona Virus (COVID-19) Tweets Dataset

description: This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose.Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details:corona_tweets_031718.csv: 831,327 tweets (Friday, March 20, 2020 1:37:15.711 AM GMT+05:45 to Friday, March 20, 2020 10:28:19.305 AM GMT+05:45)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/8sxt-dj16
title: Corona Virus (COVID-19) Tweets Dataset

description: This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose.Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details:corona_tweets_031718.csv: 831,327 tweets (Friday, March 20, 2020 1:37:15.711 AM GMT+05:45 to Friday, March 20, 2020 10:28:19.305 AM GMT+05:45)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3716047
title: Data from: Non-random association of MHC-I alleles in favor of high diversity haplotypes in wild songbirds revealed by computer-assisted MHC haplotype inference using the R package MHCtools

description: <strong>Data set from:</strong> Roved J., Hansson B., Stervander, M., Hasselquist D., & Westerdahl H. (2020). Non-random association of MHC-I alleles in favor of high diversity haplotypes in wild songbirds revealed by computer-assisted MHC haplotype inference using the R package MHCtools.
DOI: 10.17632/h9nw4cxcs8.2
title: A neural network for the molecular discrimination of pulmonary arterial hypertension (PAH) and pulmonary veno-occlusive disease (PVOD)

description: This feed-forward neural network for discriminating PAH from PVOD is based on the R packages 'caret' and 'nnet'. It was trained on transcriptomics data acquired with the NanoString nSolver technology. Please see the referenced article for further information! The .rds file contains the serialized model. In order to read the model into an R environment the following steps have to be performed: - Start an R terminal - Execute the 'readRDS' function with the (relative) path to the RDS file as only option and store the returned object in a variable - The returned object is of the class caret::train and can directly be used for the classification of samples (given that the data has been identically prepared/normalized) Please see the manuals of the R packages 'caret' and 'nnet' for help on how to use the loaded objects. Examplary R commands: model.caret
DOI: 10.5281/zenodo.3719277
title: Bio-cementation and crackling noise from calcareous sand and consolidated aggregates

description: The supporting data for "Bio-cementation and crackling noise from calcareous sand and consolidated aggregates".
DOI: 10.5281/zenodo.3719276
title: Bio-cementation and crackling noise from calcareous sand and consolidated aggregates

description: The supporting data for "Bio-cementation and crackling noise from calcareous sand and consolidated aggregates".
DOI: 10.5281/zenodo.3719300
title: The mantle transition zone hosts the missing HIMU reservoir beneath eastern China

description: Table S1. Geochemical and isotopic compositions of Cenozoic lavas from Wudi Table S2. LA-ICP-MS analyses of clinopyroxenes from Wudi lavas, clinopyroxenes from Xenoliths in Wudi lavas, and USGS basalt glass standards Table S3. The Pb isotopic data for clinopyroxene and groudmass from Wudi lavas, and clinopyroxenes from Xenoliths in WD lavas Table S4. The compositions of olivines from Wudi lavas Table S5. The compositions of Late Cenozoic lavas with SiO2 &lt; 55 wt. % from eastern China. Table S6. Monte Carlo modeling for Pb isotope source composition from Paleo-Asian ocean crust.
DOI: 10.5281/zenodo.3719299
title: The mantle transition zone hosts the missing HIMU reservoir beneath eastern China

description: Table S1. Geochemical and isotopic compositions of Cenozoic lavas from Wudi Table S2. LA-ICP-MS analyses of clinopyroxenes from Wudi lavas, clinopyroxenes from Xenoliths in Wudi lavas, and USGS basalt glass standards Table S3. The Pb isotopic data for clinopyroxene and groudmass from Wudi lavas, and clinopyroxenes from Xenoliths in WD lavas Table S4. The compositions of olivines from Wudi lavas Table S5. The compositions of Late Cenozoic lavas with SiO2 &lt; 55 wt. % from eastern China. Table S6. Monte Carlo modeling for Pb isotope source composition from Paleo-Asian ocean crust.
DOI: 10.25919/5e74cfb14b1ee
title: Parkes observations for project P971 semester 2019OCTS_10

description: We propose timing observations of the relativistic pulsar - white-dwarf binary PSR J1141-6545. This unique system offers access to a wide range of physics, from relativistic phenomena through pulsar emission cone studies, to the structure and distribution of interstellar plasma along our line-of-sight. The recently installed Parkes Ultra Wide Band Low Frequency (UWL) receiver has opened up the possibility of performing first-ever studies of its orbital dynamics at high temporal resolution and study of the effects of the intra-binary medium on the dispersion and polarisation of the pulse profile. Our analysis of data obtained to date suggests that the pulsar would precess away from our line-of-sight in the next 3-5 years. Hence, regular observations are crucial to maximizing its science outcome
DOI: 10.25573/serc.11958996.v1
title: Dataset: Does livestock grazing affect sediment deposition and accretion rates in salt marshes?

description: Accretion rates, defined as the vertical growth of salt marshes measured in mm per year, may be influenced by grazing livestock in two ways: directly, by increasing soil compaction through trampling, and indirectly, by reducing aboveground biomass and thus decreasing sediment deposition rates measured in g/m<sup>2</sup> per year. Although accretion rates and the resulting surface elevation change largely determine the resilience of salt marshes to sea-level rise (SLR), the effect of livestock grazing on accretion rates has been little studied. Therefore, this study aimed to investigate the effect of livestock grazing on salt-marsh accretion rates. We hypothesize that accretion will be lower in grazed compared to ungrazed salt marshes. In four study sites along the mainland coast of the Wadden Sea (in the south-eastern North Sea), accretion rates, sediment deposition rates, and soil compaction of grazed and ungrazed marshes were analysed using the 137Cs radionuclide dating method. Accretion rates were on average 11.6 mm yr<sup>-1</sup> during recent decades and thus higher than current and projected rates of SLR. Neither accretion nor sediment deposition rates were significantly different between grazing treatments. Meanwhile, soil compaction was clearly affected by grazing with significantly higher dry bulk density on grazed compared to ungrazed parts. Based on these results, we conclude that other factors influence whether grazing has an effect on accretion and sediment deposition rates and that the effect of grazing on marsh growth does not follow a direct causal chain. It may have a great importance when interacting with other biotic and abiotic processes on the marsh.<br>
DOI: 10.25345/c50m60
title: MassIVE MSV000085123 - Microbispora corallina and Nonomuraea
title: LC-MSMS analyses of extract coltures of Microbispora corallina producing the lantibiotic NAI-107 (aka microbisporicin) and Nonomuraea sp. ATCC39727 producing the glycopeptide A40926
DOI: 10.25430/researchdata.cab.unipd.it.00000344
title: Pandey S, Cherubini P, Saurer M, Carrer M & Petit G (2020) Dataset of paper: Effects of climate change on treeline trees in Sagarmatha (Mt. Everest, Central Himalaya)

description: This dataset contains the detrended mean chronologies of ring widths of Betula utilis and Abies spectabilis, and the individual measurements of δ18O, δ13C, δ13Ccorr, Δ13C and iWUE
DOI: 10.6073/pasta/5e5ab89da56f13d7524a920de191888d
title: North Temperate Lakes LTER: Macrophyte Species at Quadrat Level - Trout Lake 1993 - current

description: These data are collected to document and characterize the submersed macrophytes of Trout Lake, to evaluate the long-term stability of this component, and to interface with investigations of other compartments of the ecosystem. Four sites along the shoreline of Trout Lake have been sampled annually in August along permanent line transects. These are quadrat-species level data. This dataset only contains information on species that were present. It includes a row for every species-quadrat present. If a quadrat is missing, that quadrat was either empty or was not sampled. Sampling Frequency: annually during summer Number of sites: 4
DOI: 10.21223/p3/77b32x/a97hgd
title: Part 4.4.4 - SP Vine Dissemination Practices (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/bcfsxz
title: Part 4.4.3 Sources , Access to, and Quality of SP Vines (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/elpbgq
title: Part 4.4.4.2 Labour Input use in SP (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/p4nufw
title: Part 4.4.4.1 Non-Labour Input use in SP (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/57dsca
title: Part 4.4.5B - SP Production and Sales (Roster) (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/hiuty7
title: Part 5 - OFSP Marketing & Market Information Sources (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/i1cn5j
title: Part 6 - Attitude, Perception, Practice & Knowledge (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/prakrq
title: Part 4.4.5A - SP Production and Sales (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/sxwbwl
title: Part 7.1 - Livestock Assets (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/rnqfwv
title: Part 8.3 - OFSP Production Constraints (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/vfjm8p
title: Part 8 - Household Food Security (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/vhnlda
title: Part 7.2 - Financial Assets (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/yfrtzv
title: Part 8.2 - Knowledge, Attitude, & Perceptions of Vitamin A (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/37sdmi
title: Part 2.3 - Frequency of Consumption of Vitamin A Rich Foods (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/6owdcs
title: Part 8.4 - SP Marketing Constraints (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/jzd7kd
title: Part 2.1 - Household Demographics (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/kcz3qj
title: Part 2.2 - Dietary Diversity (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/mf4hjk
title: Part 1 - Household Identication (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/6qyfyi
title: Part 1 - Household Identication (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/df5the
title: Part 4.2.6 - Other Crops Produced (Ghana).tab

description: :unav
DOI: 10.21223/p3/77b32x/ejvafg
title: Part 4.1B - Land Assets in ACRES (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/lrzrkb
title: Part 4.1A - Trends in Using SP (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/umay8h
title: Part 2.1 - Household Demographics (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/vzwrrk
title: Part 3 - Farmer's membership to Farmers Associations (Ghana).sav

description: :unav
DOI: 10.21223/p3/77b32x/yqwbii
title: Part 4.2 - Crops Produced (Ghana).tab

description: :unav
DOI: 10.5281/zenodo.3719224
title: Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2

description: Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2. The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with: <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> To get original file back, run <pre><code>cat detection_data.tar.gz.* &gt; detection_data.tar.gz</code></pre> GitHub link to the project: https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection
DOI: 10.5281/zenodo.3719225
title: Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2

description: Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2. The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with: <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> To get original file back, run <pre><code>cat detection_data.tar.gz.* &gt; detection_data.tar.gz</code></pre> GitHub link to the project: https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection
DOI: 10.21223/p3/77b32x/brssov
title: Part 2.2 - Dietary Diversity (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/n1zf10
title: Part 3 - Farmer's membership to Farmers Associations (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/nbebgh
title: Part 4.1A - Trends in Using SP (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/z3n7xo
title: Part 2.3 - Frequency of Consumption of Vitamin A Rich Foods (Nigeria).sav

description: :unav
DOI: 10.6073/pasta/291b65b090953788b25316a21bab21ff
title: North Temperate Lakes LTER: Macrophyte Biomass in Trout Lake Summary 1983 - current

description: These data are collected to document and characterize the submersed macrophytes of Trout Lake, to evaluate the long-term stability of this component, and to interface with investigations of other compartments of the ecosystem. Four sites along the shoreline of Trout Lake have been sampled annually in August along permanent line transects. This dataset includes biomass per m2 for individual species summarized by depth along the transect. Derived data include the mean and standard deviation of macrophyte biomass. These data are useful in determining the annual variability of the submersed macrophytes and providing information on the effects of the invasion of an introduced crayfish. Sampling Frequency: annually during summer Number of sites: 4
DOI: 10.21223/p3/77b32x/da8wxx
title: Part 4.4.1 - SP Attributes (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/dc5g0p
title: Part 4.2 - Crops Produced (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/heqodr
title: Part 4.3 - Agricultural Practices (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/svat1s
title: Part 4.1B - Land Assets in ACRES (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/vonvz0
title: Part 4.2.6 - Other Crops Produced (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/aabfim
title: Part 4.4.3 Sources , Access to, and Quality of SP Vines (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/dpaq2o
title: Part 4.4.4.2 Labour Input use in SP (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/jh1v8f
title: Part 4.4.4.1 Non-Labour Input use in SP (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/nmdtyd
title: Part 4.4.4 - SP Vine Dissemination Practices (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/pvit6u
title: Part 4.4.2 - SP Production Practices (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/2uqmo3
title: Part 5 - OFSP Marketing & Market Information Sources (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/6ovlxl
title: Part 4.4.5A - SP Production and Sales (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/rzmarl
title: Part 6 - Attitude, Perception, Practice & Knowledge (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/xqqqvf
title: Part 4.4.5B - SP Production and Sales (Roster) (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/egodes
title: Part 8.2 - Knowledge, Attitude, & Perceptions of Vitamin A (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/g0ma3n
title: Part 7.2 - Financial Assets (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/lmjudx
title: Part 7.1 - Livestock Assets (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/usj2zb
title: Part 8.3 - OFSP Production Constraints (Nigeria).tab

description: :unav
DOI: 10.21223/p3/77b32x/wyo4jd
title: Part 8 - Household Food Security (Nigeria).sav

description: :unav
DOI: 10.21223/p3/77b32x/fruhi1
title: JS Endline Survey Questionnaire.pdf

description: :unav
DOI: 10.21223/p3/77b32x/orlxrr
title: JS Data Dictionary.xlsx

description: :unav
DOI: 10.21223/p3/77b32x/zvvtxy
title: Part 8.4 - SP Marketing Constraints (Nigeria).tab

description: :unav
DOI: 10.5519/qd.ahyp4o4x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (744428 records)
DOI: 10.6073/pasta/04a11c4a669a4adc22479b727464d988
title: North Temperate Lakes LTER: Macrophyte Richness - Trout Lake 1993 - current

description: These data are collected to document and characterize the submersed macrophytes of Trout Lake, to evaluate the long-term stability of this component, and to interface with investigations of other compartments of the ecosystem. Four sites along the shoreline of Trout Lake have been sampled annually in August along permanent line transects. These data are quadrat level data. This dataset includes a row for every quadrat sampled. The RICHNESS field contains the number of species present (set to zero if the quadrat was empty). If a row is missing, that quadrat was not sampled Sampling Frequency: annually during summer Number of sites: 4
DOI: 10.21223/p3/xaw0xy/cm7n3d
title: COVA_FC_LONGREPORT_MR_RR_VERSION1_22FEB14_FINAL.xlsx

description: Diet recall data
DOI: 10.21223/p3/xaw0xy/m4hqxh
title: COVA_FC_LONGREPORT_MR_RR_VERSION2_22FEB14_FINAL.xlsx

description: Diet recall data
DOI: 10.21223/p3/xaw0xy/nrcuci
title: COVA_FC_COVERPAGE_CHILD_MR_RR_22FEB14_FINAl.tab

description: Diet recall data
DOI: 10.21223/p3/xaw0xy/tygitx
title: COVA_FC_COVERPAGE_MOTHER_MR_RR_22FEB14_FINAL.tab

description: Diet recall data
DOI: 10.21223/p3/xaw0xy/o43f3r
title: COHORT STUDY VISIT 3 QUESTIONNAIRE.pdf

description: :unav
DOI: 10.21223/p3/xaw0xy/uvfwb5
title: fc_merge_ea.sas7bdat

description: COVA Merged Data
DOI: 10.21223/p3/xaw0xy/yt5wbw
title: COVA_FC_SHORTREPORT_MR_RR_22FEB14_FINAL.xlsx

description: Diet recall data
DOI: 10.21223/p3/xaw0xy/zl7tis
title: COHORT STUDY VISIT 3 DATA DICTIONARY.txt

description: :unav
DOI: 10.21223/p3/xaw0xy/zspkk6
title: COVA_FC_SURVEY LOCATION FILE_22FEB14_FINAL.tab

description: Diet recall data
DOI: 10.5281/zenodo.3719378
title: Canopy_height_area1

description: The canopy height was derived from remote sensing data (Pleiades images) and trees height