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Table 10 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 10. Number of forest communities, and percent of total forest area by forest community in all measured plots across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands. Agroforest was not sampled in Palau, mangrove was not sampled in Guam or CNMI, and Montane rainforest only occurs in FSM.<table><tbody><tr><th>Jurisdiction</th><th>Communities</th><th>Lowland [80%CI]</th><th>Strand [80%CI]</th><th>Agroforest [80%CI]</th><th>Mangrove [80%CI]</th><th>Montane [80%CI]</th></tr></tbody><tbody><tr><th>RMI</th><td>4</td><td>39.3% [27.2-51.4%]</td><td>27.5% [17.6-37.4%]</td><td>31.7% [21.4-42%]</td><td>1.5% [0-3.4%]</td><td>-</td></tr><tr><th>FSM</th><td>5</td><td>57.5% [51.2-63.8%]</td><td>2.8% [1-4.6%]</td><td>17.9% [13.1-22.7%]</td><td>17.1% [12.2-22%]</td><td>4.6% [1.8-7.4%]</td></tr><tr><th>Palau</th><td>3</td><td>84.1% [78.1-90.1%]</td><td>4.2% [1-7.4%]</td><td>-</td><td>11.7% [6.6-16.8%]</td><td>-</td></tr><tr><th>Guam</th><td>3</td><td>94.3% [90.3-98.3%]</td><td>2.6% [0-5.2%]</td><td>3.1% [0-7.1%]</td><td>-</td><td>-</td></tr><tr><th>CNMI</th><td>3</td><td>93% [87.8-98.2%]</td><td>6.3% [1.5-10.8%]</td><td>0.7% [0-1.6%]</td><td>-</td><td>-</td></tr></tbody></table>

Table 7 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 7. Estimated percent of forest area with live canopy cover percent of greater than or equal to 50%, 80% and 90% across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Jurisdiction</th><th>% Forest area with canopy cover>=50% [80%CI]</th><th>Canopy cover>=80% [80%CI]</th><th>Canopy cover>=90% [80%CI]</th></tr></tbody><tbody><tr><th>RMI</th><td>92.3% [87.2-97.4%]</td><td>77.4% [68.3-86.5%]</td><td>50.1% [38-62.2%]</td></tr><tr><th>FSM</th><td>90.4% [86.4-90.8%]</td><td>52.5% [46-59%]</td><td>6.4% [3.2-9.6%]</td></tr><tr><th>Palau</th><td>92.7% [88.8-96.7%]</td><td>82.3% [76.6-88%]</td><td>61.6% [53.8-69.4%]</td></tr><tr><th>Guam</th><td>90.5% [85.3-95.7%]</td><td>62.9% [54.6-71.2%]</td><td>31.4% [23.9-38.9%]</td></tr><tr><th>CNMI</th><td>81.6% [72.8-90.4%]</td><td>58.9% [48.2-69.6%]</td><td>30.8% [20-41.6%]</td></tr></tbody></table>

Table 9 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 9. Number of live tree species per plot and total number of forest plots sampled for each forest community across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Forest community</th><th># of Tree species per plot [80%CI]</th><th># of Plots</th></tr></tbody><tbody><tr><th>Lowland Rainforest</th><td>7.5 [7.1-7.9]</td><td>253</td></tr><tr><th>Montane Rainforest</th><td>7.5 [6-9]</td><td>4</td></tr><tr><th>Strand Forest</th><td>3.8 [3.3-4.3]</td><td>32</td></tr><tr><th>Agroforest</th><td>3.4 [3-3.8]</td><td>44</td></tr><tr><th>Mangrove</th><td>3.4 [2.7-4.1]</td><td>24</td></tr></tbody></table>

Table 8 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 8. Estimated percent of endemic tree species, and number of endemic tree species inventoried across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Jurisdiction</th><th>% Endemic trees [80%CI]</th><th># of Endemic species</th></tr></tbody><tbody><tr><th>RMI</th><td>0%</td><td>1</td></tr><tr><th>FSM</th><td>23.6% [19.3-27.9%]</td><td>26</td></tr><tr><th>Palau</th><td>36.7% [32.4-41%]</td><td>38</td></tr><tr><th>Guam</th><td>18.4% [11.9-22.3%]</td><td>13</td></tr><tr><th>CNMI</th><td>17.1% [9.3-27.5%]</td><td>9</td></tr></tbody></table>

Table 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 1. List of Micronesia Challenge terrestrial measures, FIA data used, and analyses performed to describe each measure. BA is basal area. N/A = indicator uses different units.<table><tbody><tr><th>MC Terrestrial Measure</th><th>FIA Data Used</th><th>Area or # of Trees</th><th>% of Forest or % of Trees</th></tr></tbody><tbody><tr><th>Human disturbance</th><td>Disturbance (human and fire)</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th>Species diversity</th><td>Tree species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Dominant vascular plant species per plot</td><td>N/A</td><td>N/A</td></tr><tr><th></th><td>Tree species: relative dominance</td><td>Square feet per acre</td><td>BA/BA of All Trees</td></tr><tr><th></th><td>% Cover of understory species</td><td>Acreage</td><td>% of Forest Area</td></tr><tr><th>Forest structure</th><td>Tree DBH (diameter at breast height)</td><td># of Trees</td><td>% by DBH class</td></tr><tr><th></th><td>Tree height</td><td># of Trees</td><td>% by Height class</td></tr><tr><th></th><td>Basal area</td><td>Square feet per acre</td><td>N/A</td></tr><tr><th></th><td>Stem density (per plot and per acre)</td><td># of Trees per plot/acre</td><td>N/A</td></tr><tr><th>Invasive species</th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Invasive vegetation subplot cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th>Forest cover</th><td>% Live canopy cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th>Tree abundance</th><td>Tree species</td><td># of Trees</td><td>% of All trees</td></tr><tr><th></th><td>Tree rank order: endemics and invasives</td><td># of Trees</td><td>% of All trees</td></tr><tr><th>Mangrove stem density</th><td>Stem density (per acre)</td><td># of Trees per acre</td><td>N/A</td></tr><tr><th>Mangrove basal area</th><td>Basal area</td><td>Square feet per acre</td><td>Relative dominance</td></tr><tr><th>Forest community</th><td>Forest community</td><td>Acreage</td><td>% of Forest area</td></tr></tbody></table>

Table 6 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 6. Percent of forest area with invasive plant species present or covered with invasive plant species, and percent of all trees that are invasive across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Jurisdiction</th><th>Invasives present [80%CI]</th><th>Covered in invasives [80%CI]</th><th>Invasive trees [80%CI]</th></tr></tbody><tbody><tr><th>RMI</th><td>11.4% [4.9-17.9%]</td><td>1% [0-2%]</td><td>0</td></tr><tr><th>FSM</th><td>49.2% [43.9-54.5%]</td><td>11.3% [9-13.6%]</td><td>5.4% [2.1-8.7%]</td></tr><tr><th>Palau</th><td>13.3% [9.1-17.5%]</td><td>0.9% [0.2-1.6%]</td><td>1.2% [0-2.4%]</td></tr><tr><th>Guam</th><td>85.7% [81.8-89.6%]</td><td>39.9% [34.5-45.3%]</td><td>30.1% [23.9-36.3%]</td></tr><tr><th>CNMI</th><td>84.9% [80.4-89.4%]</td><td>54.6% [44.2-65%]</td><td>43.6% [31.8-55.4%]</td></tr></tbody></table>

Table 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 2. Source of largest forest disturbance by percent of forest area disturbed, and total percent of forest area affected by all disturbance types across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Jurisdiction</th><th>Largest disturbance type</th><th>% of Forest area [80% CI]</th><th>All disturbances [80% CI]</th><th>Endemics [80%CI]</th><th>Most common understory species</th><th>% Forest area [80%CI]</th></tr></tbody><tbody><tr><th>RMI</th><td>Weather</td><td>4.3% [0-8.6%]</td><td>8.1% [2.3-13.9%]</td><td>0%</td><td>Cocos nucifera</td><td>11.1% [7.7-14.5%]</td></tr><tr><th>FSM</th><td>Human</td><td>21.2% [16.2-26.2%]</td><td>39.2% [33.5-45.7%]</td><td>8.3% [6.9-9.7%]</td><td>Hibiscus tiliaceus</td><td>8.1% [6.3-9.9%]</td></tr><tr><th>Palau</th><td>Weather</td><td>10.9% [5.6-16.2%]</td><td>25.1% [18.2-32%]</td><td>20.6% [17.9-23.3%]</td><td>Pinanga insignis</td><td>16.5% [13-20%]</td></tr><tr><th>Guam</th><td>Animals</td><td>29.8% [22.2-37.4%]</td><td>49.1% [40.8-57.4%]</td><td>6.9% [5-8.8%]</td><td>Hibiscus tiliaceus</td><td>9.9% [7.8-12%]</td></tr><tr><th>CNMI</th><td>Tree Disease</td><td>40.1% [33.1-47.1%]</td><td>67.7% [58.7-76.7%]</td><td>9.3% [5.8-12.8%]</td><td>Leucaena leucocephala</td><td>23.5% [17.9-29.1%]</td></tr></tbody></table>

Table 5 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data

Table 5. Mean tree DBH, height, stem density, and basal area for forest across Micronesia. CI: confidence interval, FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.<table><tbody><tr><th>Jurisdiction</th><th>DBH in inches [80%CI]</th><th>Height in feet [80%CI]</th><th>Stems/acre [80%CI]</th><th>Square Feet/acre [80%CI]</th></tr></tbody><tbody><tr><th>RMI</th><td>4 [3.3-4.7]</td><td>23.1 [20.5-25.7]</td><td>726 [556-896]</td><td>124 [112-136]</td></tr><tr><th>FSM</th><td>4.4 [4.1-4.7]</td><td>27.3 [26.3-28.3]</td><td>611 [558-664]</td><td>145 [132-158]</td></tr><tr><th>Palau</th><td>3.9 [3.8-4]</td><td>26.9 [25.7-28.1]</td><td>937 [866-1008]</td><td>143 [131-155]</td></tr><tr><th>Guam</th><td>3.1 [3-3.2]</td><td>22.7 [22.2-23.6]</td><td>1014 [885-1143]</td><td>84 [78-90]</td></tr><tr><th>CNMI</th><td>2.8 [2.5-3.1]</td><td>20.8 [19.9-21.7]</td><td>1392 [1148-1636]</td><td>84 [72-96]</td></tr></tbody></table>

opencc-by-nc-4.0Sep 2024View details →

Neutron activation analysis data of pottery, clay, obsidian, and andesite from multiple sites in Argentina

Neutron activation analysis data of pottery, clay, obsidian, and andesite from multiple sites in Argentina and Chile

opencc-by-4.0Sep 2024View details →

Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".

This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020". The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.

Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS

This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.).

opencc-by-4.0Sep 2024View details →

Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"

Data and codes for the manuscript entitled: "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"   Please see Readme.txt for details

USGS National Elevation Dataset (NED) DEM reprojected into Collection 2 Landsat analysis ready data (ARD) tiles

This dataset is used to test the Classifying the raw irregular time series (CRIT) codes and model for CONUS land cover classification with a deep learning model that can directly classify Landsat irrigular time series.  See the code here https://github.com/hankui/CRIT

A Deep Learning Approach for TEM Data Denoising, Inversion and Uncertainty Analysis with Monte Carlo Dropout

This dataset includes the code and data for training the inversion network used in the study. The provided files cover data loading, preprocessing, and network training for transient electromagnetic (TEM) data inversion. For details on the included files and instructions on usage, please refer to the README.txt file.

opencc-by-4.0Aug 2024View details →

Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study

Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C. O conjunto de dados atualizado está organizado nas seguintes pastas: <ol> <li> c_programs: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas. <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> codes_suggested_by_the_tools: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode. <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> complexity_of_codes: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado. <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.

N-Periodic SSFP Data for Analysis

.dat files used for N-periodic T2* analysis

Supplementary data for analysis of TEs in Atlantic salmon genome

This dataset contains the data used to generate the figures in the manuscript "The role of transposon activity in shaping cis-regulatory element evolution after whole genome duplication". The scripts for the figures can be found at https://gitlab.com/sandve-lab/TE-CRE

openodc-byMar 2022View details →

Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion

Data from: Data-driven analysis of oscillations in Hall thruster simulations   - Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino - Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a> - Date: 2022-03-24 - Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis - Version: 1.0.4 - Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a> - License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a>   Abstract   This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 Plasma Sources Sci. Technol. 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>. Additionally, the raw simulation data is also employed in the following journal publication: Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 Mach. Learn.: Sci. Technol. 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a>   Dataset description   The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>. Please refer to the relative article for further details regarding any of the parameters and/or configurations.   Data files   The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended. The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as: <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables. The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.   Citation   Works using this dataset or any part of it in any form shall cite it as follows. The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available. Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.   Acknowledgments   This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

opencc-by-4.0Apr 2024View details →

Simulated EEG and EMG data for Reference Phase Analysis evaluation.

This dataset comprises simulated EEG (Electroencephalography) signals recorded from 29 channels along with an EMG (Electromyography) signal. The signals have been artificially generated using real EEG and EMG signals as the basis. Five distinct source models were generated, each comprising a different number of sources. We created models with 2, 3, 4, 5, and 6 sources. Furthermore, we add additive noise to the signals encompassing SNRs ranging from 90 to 0 dB and a phase jitter ranging from 0.1 to 1.5 radians. We generated EEG and EMG signals using MATLAB and employed a forward model, created with FieldTrip, to convert signals from the source space to the electrode space. EEG measures were computed utilizing a linear mixture model. The simulated dataset aims to mimic the characteristics of real EEG and EMG signals, including their temporal dynamics, noise, and frequency spectra. It serves as a valuable resource for developing and testing signal processing algorithms, and brain-computer interfaces in the fields of neuroscience and biomedical engineering.

Data supporting the article: "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia"

This dataset supports the article titled "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia.", available at <a href="https://doi.org/10.3390/microorganisms12102071">https://doi.org/10.3390/microorganisms12102071</a>. The dataset includes fecal microbiota sequencing data from adolescent patients with early-onset psychosis, adult patients with schizophrenia, and non-psychotic controls. The data were generated using 16S rRNA gene sequencing and analyzed with QIIME2 and PICRUSt2 to assess microbial diversity and functional pathways. Variables such as age, diagnosis, and medication use are included. The dataset contains: <ul> <li>Processed results from fecal microbiota analysis (OTUs and taxonomic classifications)</li> <li>Metadata associated with each sample (age, diagnosis, medication)</li> <li>Results from diversity analysis (alpha and beta diversity metrics)</li> <li>Functional analysis of microbial pathways (PICRUSt2)</li> </ul> These data are essential for reproducing the findings discussed in the article. Note that the raw sequencing data are available upon request.