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6,569 results for “Long-term”
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Data-Driven Study of Long-Term Gaming Experience (Tribalwars)
<p>This is the accompanying dataset for our Paper "Data-Driven Study of Long-Term Gaming Experience" at the 14th International Conference on Quality of Multimedia Experience (QoMEX) - Please city this work if you use this dataset.</p> <p>This dataset contains hourly crawled player metadata of the Tribalwars casual game game round "dep13".</p> <p>With this dataset and a set of possible tooling, we hope to provide a foundation into further data-driven QoE studies.</p> <p>You will find further information about prerequisites and the dataset within the <strong>eval.ipynb</strong>.</p>
Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)
<p>Database with Wi-Fi samples (RSSI measurements) collected from several Raspberry Pi (RPi) 3B+ devices continuously over 2+ years. The database includes the long-term dataset from the RPi devices (with 7,435,398 Wi-Fi samples), as well as 12 site-survey datasets (with 11,140 Wi-Fi samples) conducted in this period. The site-surveys were also conducted with a RPi 3B+.</p> <p>The measurements obtained from the RPi 3B+ Wi-Fi interface include the list of detected APs, their signal strength (RSSI) and transmission channel. The list has APs from the 2.4GHz and 5GHz bands because it supports IEEE 802.11.b/g/n/ac wireless LAN. </p> <p>These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021.</p> <p>The supporting material includes the Python scripts to parse and analyse the data by generating various plots. It also includes the locations of the monitoring devices and the list of reference points considered in the site-surveys.</p> <p> </p> <p>A detailed description of this dataset and the data collection process can be found here:</p> <p>Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. <em>Sensors</em>. <strong>2022</strong>; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a></p> <p> </p> <p>When using this dataset, please add a citation to the paper above or this citation:</p> <p>Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. <a href="Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6928554">https://doi.org/10.5281/zenodo.6928554</a> </p> <p> </p> <p>The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting:</p> <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," <em>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Lloret de Mar, Spain, 2021, pp. 1-8, doi: 10.1109/IPIN51156.2021.9662558.</li> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Overcoming Radio Map Degradation in Wi-Fi-based Positioning Systems," <em>2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>
Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network
<p><strong>Data Description</strong>:</p> <p>The USDA Long-Term Agroecosystem Research (LTAR) Network coordinates agricultural research across 18 research sites in the conterminous United States (CONUS). However, it is unclear how well these sites represent the totality of agricultural working lands within the CONUS. Therefore, we performed a quantitative analysis of the 18 sites, based on 15 climatic and edaphic characteristics, to produce maps of representativeness and constituency across the CONUS. Representativeness shows how well the combination of environmental drivers at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location.</p> <p>Files in collection (22):</p> <p>Collection contains 11 geospatial rasters and 11 PNGs visualizing them.</p> <p>TIF files:</p> <p>├── conus_ltar_constituency_workinglands.tif [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.tif [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.tif [Principal Component 1]<br> ├── conus_ltar_v5.pc2.tif [Principal Component 2]<br> ├── conus_ltar_v5.pc3.tif [Principal Component 3]<br> ├── conus_ltar_v5.pc4.tif [Principal Component 4]<br> ├── conus_ltar_v5.pc5.tif [Principal Component 5]<br> ├── conus_ltar_v5.pc6.tif [Principal Component 6]<br> ├── conus_ltar_v5.pc7.tif [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.tif [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.tif [Representativeness of combined LTAR + NEON + LTER networks]</p> <p>PNG files:</p> <p>├── conus_ltar_constituency_workinglands.png [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.png [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.png [Principal Component 1]<br> ├── conus_ltar_v5.pc2.png [Principal Component 2]<br> ├── conus_ltar_v5.pc3.png [Principal Component 3]<br> ├── conus_ltar_v5.pc4.png [Principal Component 4]<br> ├── conus_ltar_v5.pc5.png [Principal Component 5]<br> ├── conus_ltar_v5.pc6.png [Principal Component 6]<br> ├── conus_ltar_v5.pc7.png [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.png [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.png [Representativeness of combined LTAR + NEON + LTER networks]</p> <p><strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution, while the geospatial visualizations are provided in PNG format.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code class="language-bash">GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p><strong>Category labels for Constituency data</strong>:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Cat</th> <th scope="col">LTAR Siite</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Archbold-University of Florida</td> </tr> <tr> <td>2</td> <td>Central Mississippi River Basin</td> </tr> <tr> <td>3</td> <td>Central Plains Experimental Range</td> </tr> <tr> <td>4</td> <td>Eastern Corn Belt</td> </tr> <tr> <td>5</td> <td>Great Basin</td> </tr> <tr> <td>6</td> <td>Gulf Atlantic Coastal Plain</td> </tr> <tr> <td>7</td> <td>Jornada Experimental Range</td> </tr> <tr> <td>8</td> <td>Kellogg Biological Station</td> </tr> <tr> <td>9</td> <td>Lower Chesapeake Bay</td> </tr> <tr> <td>10</td> <td>Lower Mississippi River Basin</td> </tr> <tr> <td>11</td> <td>Northern Plains</td> </tr> <tr> <td>12</td> <td>Platte River High Plains Aquifer</td> </tr> <tr> <td>13</td> <td>R.J. Cook Agronomy Farm</td> </tr> <tr> <td>14</td> <td>Southern Plains</td> </tr> <tr> <td>15</td> <td>Texas Gulf</td> </tr> <tr> <td>16</td> <td>Upper Chesapeake Bay</td> </tr> <tr> <td>17</td> <td>Upper Mississippi River Basin</td> </tr> <tr> <td>18</td> <td>Walnut Gulch Experimental Watershed</td> </tr> </tbody> </table> <p><strong>Paper describing data and methods</strong>:</p> <p>Kumar, J., Coffin, A. W., Baffaut, C., Ponce-Campos, G. E., Witthaus, L., & Hargrove, W. W. (2023). Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks. In Environmental Management. Springer Science and Business Media LLC. https://doi.org/10.1007/s00267-023-01834-9</p> <p> </p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Precomputed data accompanying the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics"
<p>Together with the python code in <a href="https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence" target="_blank" rel="noopener">https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence</a>, this dataset lets the user interact with and reproduce the results in the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics".</p> <p> All data in this dataset is computed with the provided code.</p>
Long-term moss monitoring network for atmospheric deposition in Germany, link to research data and scientific software
<p>Research data and scientific software related to a study that aims to restructure a long-term monitoring network using moss as biomonitor for atmospheric deposition in Germany. Data from the European Moss Survey 2005 and a statistically based methodology including a decision support system were used to design the spatial network for the 2005 survey.</p>
The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA
<p><strong>Related paper containing detailed description</strong>: <strong><a href="https://essd.copernicus.org/articles/12/177/2020/essd-12-177-2020.html">Moesinger et al. (2020)</a></strong></p> <p>Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management.</p> <p>We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.</p> <p>There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files.</p> <p> </p> <p>Currently there is an issue with opening the file using ESA SNAP. As an alternative <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a> can be used to quickly visualize the data. </p> <p>An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020.</p> <p> </p> <p><strong>Please contact us if you have any questions, problems or suggestions for improvement!</strong></p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>"VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): <ul> <li>VODCA C-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): <ul> <li>VODCA X-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : <ul> <li>VODCA Ku-band files, sorted into yearly folders</li> </ul> </li> <li>"vodca_v01-0_K-band_2007-06-01.nc" <ul> <li>sample file of the Ku-band product</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Variables of data in VODCA files:</strong></p> <ul> <li>"VOD": Unitless, Vegetation Optical Depth of the respective band</li> <li>"sensor_flag": Bit-flag indicating which sensors contributed to each observation. <ul> <li>Values: <ul> <li>1 = AMSR-E</li> <li>2 = AMSR2</li> <li>3 = SSM/I F8</li> <li>4 = SSM/I F11</li> <li>5 = SSM/I F13</li> <li>6 = TMI</li> <li>7 = WindSat</li> </ul> </li> </ul> </li> <li>"processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations <ul> <li>Values: <ul> <li>0 = Everything is fine</li> <li>10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz</li> <li>11 = Sensor is scaled to matched TMI instead of AMSR-E</li> <li>12 = Sensor scaled without temporally overlapping observations</li> </ul> </li> </ul> </li> <li>"time"/"lon"/"lat": Dimensions of the data.</li> </ul> <p> </p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
The long-term impact of church activity on social capital: lessons from post-war Czechoslovakia
<p>This is a basic set of variables used in the paper "The long-term impact of church activity on social capital: lessons from post-war Czechoslovakia", co-authored by Štěpán Mikula (Masaryk Unversity, IZA), Fabio Sabatini (Sapienza University of Rome, IZA), and Tommaso Reggiani (Cardiff University, Masaryk Unversity, IZA). The first draft of the paper is available as a MUNI ECON working paper (<a href="https://doi.org/10.5817/WP_MUNI_ECON_2023-03" target="_blank" rel="noopener">DOI 10.5817/WP_MUNI_ECON_2023-03</a>), which also includes a detailed data description.</p> <p>The current dataset is restricted to variables that are available from open sources. We are not allowed to share the complete set of variables publically.</p> <p>The table contains the following variables:</p> <ul> <li>KOD_OBEC...Municipality ID defined by <a href="https://apl2.czso.cz/iSMS/cisdet.jsp?kodcis=43">CISOB codebook</a>.</li> <li>dieceze...Diocese name (as defined in pre-1989 period)</li> <li>year</li> <li>church...Indicator variable for the presence of a church building in the municipality in 1945</li> <li>ngo_all...Density of NGOs</li> <li>ngo_other...Density of church-unrelated NGOs</li> <li>ngo_church...Density church-related NGOs</li> <li>priest_years...priests' tenure (years)</li> <li>ger_share...Share of ethnic Germans in 1930 (<a href="https://doi.org/10.1016/j.jue.2021.103385">Guzi et al. 2021</a>)</li> <li>population1950...Population in 1950 (<a href="https://csu.gov.cz/produkty/historicky-lexikon-obci-1869-az-2015">Historický atlas</a>)</li> <li>TRI_mean...Terrain Roughness Index</li> <li>CZdist...Distance to the country border</li> </ul>
Evaluating F10.7 and F30 Radio Fluxes as Long-Term Solar Proxies of Energy Deposition in the Thermosphere
<p><span>We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes represent solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of F10.7 during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. Therefore, F30 is a more consistent proxy than F10.7 during the last 60 years. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.</span></p>
Coral growth data for the research article "Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hotspot" in Journal of Animal Ecology
<p>This repository contains the coral growth data files used to generate the results for the following article:</p> <p> </p> <p>MJ. Vergotti, JP. D’Olivo, T. Brachert, P. Capdevila, J. Garrabou, C. Linares, P. Spreter, DK. Kersting (2024) Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hot-spot. <em>Journal of Animal Ecology</em>. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.14225</p> <p> </p> <p><strong>Abstract: </strong>The impact of warming on zooxanthellate corals is widespread, from tropical to temperate seas, with their associated mortalities causing global concern. The temperate coral <em>Cladocora caespitosa</em> is the only zooxanthellate coral with reef-building capacity in the Mediterranean Sea, a climate change hotspot with warming rates triple the global average. Over the past two decades, <em>C. caespitosa</em> populations have suffered severe mortality events associated with marine heatwaves (MHWs). However, with monitoring efforts beginning, at best, in the 2000s, the occurrence of MHWs before to that period, as well as the sub-lethal effects of these events remain poorly understood. Here we use sclerochronology to reconstruct the histories of past stress events and long-term sub-lethal effects on <em>C. caespitosa</em> in three locations within the NW Mediterranean Sea, each with different environmental conditions. Skeletal extension, density and calcification rates were compared to the <em>in situ</em> seawater temperature of each site to assess their relationship. Additionally, we assessed the occurrence of skeletal growth anomalies to reconstruct stress events between 1991 and 2021, a period that encompasses the onset and evolution of warming-related mass mortality events in the NW Mediterranean Sea. Our results reveal a positive association between calcification and temperature, following a latitudinal temperature gradient. However, the evolution of the likelihood distribution of growth rates in the warmest site (Columbretes Islands) since the 1990s indicates a decrease in linear extension and calcification rates during the most recent years. With the increase in the frequency of MHWs and growth anomalies during the last decade, this decline suggests a recurrence in physiological stress events. These results unravel information on the long-term impacts of warming on coral growth and highlight the potential of applying sclerochronology to reconstruct sub-lethal effects of warming using <em>C. caespitosa</em>. </p> <p> </p> <p> </p> <p><strong>Funding</strong>: This research is supported by the Horizon 2020 program of research and innovation of the European Union under the MaCoBioS grant agreement, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 401447620) and by the Spanish Ministry of Science, Innovation and Universities under the project UndResCoral (project no. PID2022-137539OA-C22). D.K.K. was supported by a Ramon y Cajal postdoctoral grant funded by the Ministry of Science and Innovation (PEICTI 2021–2023; grant no. RYC2021-033576-I). C.L. acknowledges the support by ICREA Academia. J.G. acknowledges the grant “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033.</p>
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
Durability in materials jetting: A long-term study
<p>This file contains data for the paper "Durability in materials jetting: A long-term study"<br> For more information, please contact Ali Payami Golhin (payami.ag@gmail.com)</p>
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