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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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Communities</b></th><th><b>Lowland [80%CI]</b></th><th><b>Strand [80%CI]</b></th><th><b>Agroforest [80%CI]</b></th><th><b>Mangrove [80%CI]</b></th><th><b>Montane [80%CI]</b></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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Forest area with canopy cover>=50% [80%CI]</b></th><th><b>Canopy cover>=80% [80%CI]</b></th><th><b>Canopy cover>=90% [80%CI]</b></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

<p>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.</p><table><tbody><tr><th><b>Forest community</b></th><th><b># of Tree species per plot [80%CI]</b></th><th><b># of Plots</b></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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>% Endemic trees [80%CI]</b></th><th><b># of Endemic species</b></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

<p>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.</p><table><tbody><tr><th><b>MC Terrestrial Measure</b></th><th><b>FIA Data Used</b></th><th><b>Area or # of Trees</b></th><th><b>% of Forest or % of Trees</b></th></tr></tbody><tbody><tr><th><i>Human disturbance</i></th><td>Disturbance (human and fire)</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Species diversity</i></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><i>Forest structure</i></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><i>Invasive species</i></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><i>Forest cover</i></th><td>% Live canopy cover</td><td>Acreage</td><td>% of Forest area</td></tr><tr><th><i>Tree abundance</i></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><i>Mangrove stem density</i></th><td>Stem density (per acre)</td><td># of Trees per acre</td><td>N/A</td></tr><tr><th><i>Mangrove basal area</i></th><td>Basal area</td><td>Square feet per acre</td><td>Relative dominance</td></tr><tr><th><i>Forest community</i></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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Invasives present [80%CI]</b></th><th><b>Covered in invasives [80%CI]</b></th><th><b>Invasive trees [80%CI]</b></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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>Largest disturbance type</b></th><th><b>% of Forest area [80% CI]</b></th><th><b>All disturbances [80% CI]</b></th><th><b>Endemics [80%CI]</b></th><th><b>Most common understory species</b></th><th><b>% Forest area [80%CI]</b></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><i>Cocos nucifera</i></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><i>Hibiscus tiliaceus</i></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><i>Pinanga insignis</i></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><i>Hibiscus tiliaceus</i></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><i>Leucaena leucocephala</i></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

<p>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.</p><table><tbody><tr><th><b>Jurisdiction</b></th><th><b>DBH in inches [80%CI]</b></th><th><b>Height in feet [80%CI]</b></th><th><b>Stems/acre [80%CI]</b></th><th><b>Square Feet/acre [80%CI]</b></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-4.0Sep 2024View details →

GWAS summary statistics for 56 NMR metabolites measured in 246,683 UK Biobank participants

<p>GWAS summary statistics for 56 metabolites measured in 246,683 UK Biobank participants using the Nightingale Health platform. GWAS was performed using the https://github.com/AlasooLab/reGSusie workflow.</p>

opencc-by-4.0Sep 2024View details →

TRADE4SD Deliverable 2.3: Database and infographics on standards rapprochement: STCs and bilateral measure of distance on pesticides and antibiotics

<p><span>One of the objectives of the Horizon2020 project “TRADE4SD” is to offer policy recommendations for improving trade policies at the national, European, and global levels, including reforms to the WTO, and to enhance policy alignment. To achieve this goal, it is essential to address the impact of NTMs, which, despite their increasing use, remain largely underexplored in terms of the effects on international trade. This limited understanding is due to the complexity of NTMs and their effects are difficult to generalise. Several critical areas related to NTMs and their implications for international trade require further investigation, which “TRADE4SD” seeks to address. This work explores how NTMs affect market access for developing countries, as these nations may face various challenges, such as limited capabilities, technological gaps, weaker infrastructures and institutions, and asymmetric information. More specifically, “TRADE4SD” aims to identify best practices for improving the management of SPS, which are essential for enhancing the competitiveness of agricultural and food exports. Strengthening SPS capacity is also vital for boosting productivity in the agricultural and food processing industries, contributing to agricultural and rural development, and helping to alleviate poverty. </span></p> <p><span>This deliverable consists of two main sections: one addressing Maximum Residue Levels (MRLs) for pesticides and antibiotics and the other focusing on Specific Trade Concerns (STCs). </span></p> <p><span>In the first section, we analysed the regulations and then acquired and processed the data to create the databases, which we later used to develop the indices and infographics (for both pesticides and antibiotics).</span></p> <p><span>In the STCs section, we analysed WTO documentation on all open disputes and verified their status. During our analysis, we identified the relationships between STCs and the Sustainable Development Goals (SDGs). Finally, we developed the infographics.</span></p>

Implementation of a reconstructed spectral sky definition in a light simulation tool and comparison to measurements

<p>Lark is a spectral light simulation tool that emerged to optimize indoor light exposure for ipRGC-influenced light (IIL) responses which play a significant role in our physiological and psychological health. Lark provides two sky spectrum models – D65 Standard Illuminant and measurements of sky spectral power distribution, both of which present limitations. D65 Standard Illuminant is an average sky spectrum based on measurements taken in locations in 45°N–55°N latitude range and may result in inaccuracies for locations outside of this range and for certain times of the day and year. Measurements of sky spectral power distribution, on the other hand, are very accurate, however, inaccessible to most users due to cost of equipment and lack of publicly available data. These limitations can be addressed by Occupant Wellbeing through Lighting (OWL), another spectral light simulation tool. OWL reconstructs a spectral power distribution for a given time and location, and its components can be implemented in Lark interface. The dataset presented here shows the results of the study that compared the impact of these three sky spectrum models on accuracy of Lark simulation tools for prediction of IIL responses.</p>

opencc-by-4.0Aug 2024View details →

Additional figures: Obliquities of exoplanet host stars. Nineteen new and updated measurements, and trends in the sample of 205 measurements

<p>Here we provide some additional figures as supporting material to Knudstrup et al. (2024).</p> <p><strong>Phase-folded light curves: </strong>The best-fitting models are shown as the white line in the top panel and the residuals are given in the bottom panel.</p> <p><strong>HD 118203 b:</strong> TESS 2 min. cadence data shown as gray points with error bars. Black markers are binned data in ~10 min. intervals.</p> <p><strong>HD 149193 b:</strong> TESS light curves 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>K2-261 b:</strong> K2 light curve in black, TESS 20 sec. and 2 min. in light gray and gray, respectively. A light curve for each passband is shown.</p> <p><strong>K2-287 b:</strong> K2 30 min cadence shown in orange with CHEOPS 1 min. and ground-based 2 min. observations in light gray and gray, respectively. A light curve for each passband is also shown with a matching color. </p> <p><strong>KELT-3 b:</strong> TESS 2 min. cadence data shown in gray.</p> <p><strong>KELT-4Ab:</strong> TESS 2 min. cadence data shown in gray.</p> <p><strong>LTT 1445Ab:</strong> Black TESS 2 min. and gray TESS 20 sec.</p> <p><strong>TOI-451Ab:</strong> Unbinned 2 min. cadence data are shown in gray and in black the binned (~6 min.) data are shown.</p> <p><strong>TOI-813 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>TOI-892 b:</strong> TESS 2 min. and 30 min. cadence data are shown in gray and black, respectively.</p> <p><strong>TOI-1130 c:</strong> TESS 20 sec. and 30 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-50 b:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-59 b:</strong> TESS 2 min. cadence data shown in gray and ground-based 2 min. KeplerCam photometry in orange.</p> <p><strong>WASP-136 b:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-172 b:</strong> TESS 20 sec. and 30 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-173Ab:</strong> TESS 20 sec. and 2 min. cadence data shown in gray and black, respectively.</p> <p><strong>WASP-186 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>XO-7 b:</strong> TESS 20 sec., 2 min., and 30 min. cadence data are shown in light gray, gray, and black, respectively.</p> <p><strong>WASP-148 b:</strong> The MuSCAT-2 photometry obtained simultaneous with our spectroscopic transit observations. The observations in the different <em>griz</em> filters are shown as blue, orange, green, and red markers, respectively.</p> <p><strong>WASP-26 b:</strong> Unbinned 20 sec. cadence data from TESS are shown in gray and in black the binned (~6 min.) data are shown.</p> <div> <div> <div> <p> </p> <p><strong>Peak of the stacked CCFs: </strong>Similar to Fig. A.8. we show the peaks of the stacked CCFs for KELT-4 and XO-7 created by assuming the best-fitting value of b from the RV-RM fit.</p> <p><strong>KELT-4A:</strong>  The gray contours show a peak around (v sin i⋆,λ)=(6.0 km/s,90 deg). The red contours are the same as created from the posterior shown to the right in Fig. A.9.</p> <p><strong>XO-7: </strong> The gray contours show a peak around (v sin i⋆,λ)=(4 km/s,-60 deg). The red contours are the same as created from the posterior shown to the right in Fig. A.28.</p> <p> </p> </div> </div> </div> <p><strong>References:</strong></p> <div> <div> <div> <p>Knudstrup, E., Albrecht, S. H., Winn, J. N., et al., 2024, arXiv:2408.09793</p> </div> </div> </div>

PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"

<p>The dataset contains 4 different files: </p> <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>

Meteorological Data from Kardamyla, Chios: March 2024 Baseline Measurements for the MUSICA Project

<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from March 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.1°C, recorded on March 31st, 2024, at 15:10.</li> <li><strong>Lowest temperature</strong>: 3.3°C, recorded on March 24th, 2024, at 05:50.</li> <li><strong>Highest daily rainfall</strong>: 28.2 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 77.2 km/h, recorded on March 12th, 2024, at 00:10.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>

Meteorological Data from Kardamyla, Chios: February 2024 Baseline Measurements for the MUSICA Project

<p>The present meteorological data is collected from the weather station in <strong>Kardamyla</strong>, a village in northern Chios, and is published on the <strong>Zenodo</strong> platform for open access. The Kardamyla area was selected due to its proximity to the location where the <strong>MUSICA</strong> project platform will be installed. The data includes measurements of temperature, rainfall, wind speed, and wind direction, and covers the period from February 1st to February 29th, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, with the goal of monitoring climate changes in the Kardamyla area and the broader region of Chios, particularly following the installation of the project's platform. The data presented here covers the period from February 2024, and new measurements will be regularly added as part of ongoing monitoring efforts to track changes in weather and climate conditions.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for February 2024</h3> <ul> <li><strong>Highest temperature</strong>: 21.9°C, recorded on February 29th, 2024, at 13:40.</li> <li><strong>Lowest temperature</strong>: 3.0°C, recorded on February 4th, 2024, at 07:30.</li> <li><strong>Highest daily rainfall</strong>: 49.4 mm, recorded on February 12th, 2024.</li> <li><strong>Highest wind speed</strong>: 82.1 km/h, recorded on February 11th, 2024, at 21:30.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>

Meteorological Data from Chios: March 2024 Baseline Measurements for the MUSICA Project

<p>The present meteorological data is collected from the weather station in <strong>Chiostown</strong>, located in Chios, and is published on the <strong>Zenodo</strong> platform for open access. The station is positioned at an elevation of 32 meters and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from March 1st to March 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data presented here for March 2024 provides insight into the weather conditions leading up to the installation of the project's platform. Continuous monitoring efforts will track changes in weather and climate conditions as the project progresses.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for March 2024</h3> <ul> <li><strong>Highest temperature</strong>: 25.3°C, recorded on March 31st, 2024, at 15:50.</li> <li><strong>Lowest temperature</strong>: 7.1°C, recorded on March 24th, 2024, at 03:20.</li> <li><strong>Highest daily rainfall</strong>: 25.4 mm, recorded on March 5th, 2024.</li> <li><strong>Highest wind speed</strong>: 66.0 km/h, recorded on March 12th, 2024, at 10:50.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena.</p>

opencc-by-4.0Apr 2024View details →

CO2 fluxes measurement data set for the lower segment of the River Noce, Italy

<p>This data set reports on data recorded during four measurement campaigns in the lower segment of the River Noce, Italy.<br>The measurements were aimed at characterising CO2 fluxes along the river, and were conducted using a combination of loggers (time-resolved data) and sampling (space-resolved data).<br>The data were collected in the Santa Giustina reservoir (upstream reservoir), along the residual flow reaches that extend between the Santa Giustina reservoir and the outlet of the hydropower diversion system in Mezzocorona, and downstream of the diversion outlet.</p> <p>The data is provided as .csv and Matlab .mat files.<br>For more information about the data set and data acquisition, please contact the author or refer to the following article:</p> <p><br>Dolcetti, G., Piccolroaz, S., Bruno, M. C., Calamita, E., Larsen, S., Zolezzi, G. & Siviglia, A. Quantification of carbopeaking and CO2 fluxes in a regulated Alpine river.</p>

Stroboscopic FFDXM measurement on a one-port SAW resonator device

<p>The raw data were in .edf format. The number of raw data files exceeds the limit of Zenodo uploads (100 files), and for that reason a combined .npy file was generated and shared instead.</p>

opencc-by-4.0Aug 2024View details →

Additional tables: Obliquities of exoplanet host stars. Nineteen new, updated measurements for the sample of 205 measurements and observed trends.

<p>Here we provide some additional tables as supporting material to Knudstrup et al. (2024).</p> <p>The tables are given as .tex files and can be compiled by including the additional files (.bib, .cls, etc.).</p> <p><strong>mcmc_post.tex</strong> contains a selection of the posteriors resulting from the MCMCs we carried out in Appendix A of Knudstrup et al. (2024).</p> <p><strong>literature.tex</strong> contains extensions to Tables A1 and A2 of Albrecht et al. (2022), which we have used for our analyses in Section 4 of Knudstrup et al. (2024).</p> <p><strong>dfm_post.tex</strong> contains the posteriors from the different runs presented in Section 4.1 of Knudstrup et al. (2024).</p> <p><strong>rvs.tex</strong> contains an example of the format for the radial velocities used in Knudstrup et al. (2024), which are available at CDS <a title="RVs" href="https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/690/A379" target="_blank" rel="noopener">here</a>.</p> <p> </p> <h2>Update v3:</h2> <p>Added .csv files for the extensions to Tables A1 (<strong>planets.csv</strong>) and A2 (<strong>stars.csv</strong>) of Albrecht et al. (2022).</p> <p> </p> <p><strong>References:</strong></p> <div> <div> <div> <p>Albrecht, S. H., Dawson, R. I., & Winn, J. N. 2022, PASP, 134, 082001</p> <p>Knudstrup, E., Albrecht, S. H., Winn, J. N., et al., 2024, A&A, 690, A379</p> </div> </div> </div>

Measurement report: Unexpected high volatile organic compounds emission from vehicles on the Tibetan Plateau Dataset

<p>This dataset includes various emission profiles and related data, specifically:</p> <ol> <li> <p><strong>Source Profile Data at Different Altitudes</strong>.</p> </li> <li> <p><strong>Emission Factor Data</strong>.</p> </li> <li> <p><strong>Emission Ratio Data</strong>.</p> </li> <li><strong>Source Profile Data from PMF Source Apportionment</strong>: Data obtained through Positive Matrix Factorization (PMF), revealing the composition of emission sources.</li> <li> <p><strong>Average Profiles of Gasoline Vapors</strong>: Derived from sealed housing evaporative determination (SHED) tests, with references 1-7.</p> </li> <li> <p><strong>Average Profiles of Gasoline Vehicle Exhaust</strong>: Based on dynamometer tests, with references 2, 8-13.</p> </li> <li> <p><strong>Average Profiles of Vehicular Emissions</strong>: Collected from low-altitude tunnel measurements, reflecting emissions in real-world driving scenarios, with references 14-24.</p> </li> </ol>